ICT Roles at Absa Bank Limited

Job Summary

• Absa Group’s Chief Data Analytics and Applied AI Office (CDAIO) requires a technically exceptional and commercially grounded AI Platform Engineer (Cloud) to design, build, operate, and continuously optimise the multi-cloud AI infrastructure that powers the bank's enterprise AI capability. The AI capability must enable the CDAIO to fulfil its mandate as steward of the bank’s AI capabilities through the end-to-end delivery of the AI platform enablement, governance and acceptable use in service of the bank’s strategic and commercial objectives.
• This role is the engineering backbone of a platform that supports various live AI projects across four business units (CIB, PPB, BB, and AR) and ten countries.
• This role demands deep technical mastery in cloud AI infrastructure, AI FinOps, zero-trust security architecture, agentic AI infrastructure, and platform observability, combined with the commercial fluency to govern AI compute costs at enterprise scale and communicate trade-offs to senior business and finance stakeholders. The role includes but not limited to applying critical thinking, design thinking, and problem-solving skills in an agile team environment to solve complex platform engineering challenges, delivering high-quality solutions at optimal cost to serve, in full compliance with Absa's Enterprise-Wide Risk Management Framework, Group Architecture standards, and AI Responsible Use Policy.
• The successful candidate carries full accountability for building high-performing, scalable, enterprise-grade Platform services. As well as build capability in others to do the same.

Job Description
KEY FOCUS AREAS

• AI Platform Engineering and Architecture: Design and operation of enterprise-grade, multi-cloud AI platform infrastructure supporting bank-wide AI delivery at scale across the AI platform stack (AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, and GPU clusters).
• AI FinOps and Compute Cost Governance: Full accountability for AI compute cost models, chargeback and showback frameworks, provisioned throughput optimisation, and monthly cost-per-use-case reporting to Group Finance across all four business units.
• Platform Observability and SLA Engineering: AI-specific service reliability standards, observability tooling, and incident management for production AI workloads serving 43 live projects across ten countries.
• AI Security Architecture and Zero Trust: Zero-trust security design, OAuth / OIDC integration, prompt injection controls, and data residency compliance protecting Absa's AI platform across the different country jurisdictions.
• Agentic AI Infrastructure: Design and operation of the infrastructure layer enabling multi-agent AI systems, autonomous workflows, tool-calling architectures, and agent orchestration at enterprise scale.

Accountabilities
Platform Engineering and Architecture

• Lead the design, deployment, and continuous optimisation of Absa's multi-cloud AI platform stack: AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face Model Hub, and on-demand GPU clusters.
• Architect scalable, resilient, and reusable platform components including AI Gateway configuration, model serving infrastructure, vector database deployments, and data pipeline integration to support bank-wide AI delivery.
• Define and maintain infrastructure-as-code (IaC) standards (e.g. using Terraform or Pulumi), enabling repeatable, auditable multi-cloud AI deployments across Absa's operating territories (10 countries).
• Lead the design and operation of agentic AI infrastructure: orchestration runtime environments (e.g. Microsoft Foundry Agent Service, AWS Bedrock Agents), tool-calling schemas, agent memory and state management patterns, and multi-agent communication protocols.
• Develop and enforce cloud-agnostic model serving patterns to reduce platform lock-in and ensure workload portability across the CDAIO's multi-vendor stack.
• Identify and select appropriate internal and external technologies to deliver AI platform services; apply excellent judgement in continuously improving platform engineering practices.
• Take full accountability for end-to-end platform quality, completeness, and user experience across the development, deployment, and operational lifecycle.
• Positively contribute to the design and evolution of Group Architecture, infrastructure standards, and AI platform governance frameworks

AI FinOps and Compute Cost Governance

• Own the AI compute cost model for the CDAIO, including chargeback and showback frameworks for Databricks DBU consumption, AWS Bedrock token-based pricing, Azure AI Foundry provisioned throughput units, and GPU cluster utilisation across all four business units.
• Design and maintain FinOps dashboards and cost attribution reports using AWS Cost Explorer, Databricks System Tables cost analytics, and Azure OpenAI utilisation tooling — providing monthly cost-per-use-case reporting to Group Finance and the CDAIO COO.
• Evaluate and manage provisioned throughput versus on-demand consumption trade-offs for production AI workloads, presenting optimisation recommendations to the CDAIO and BU technology leads.
• Identify and execute AI compute cost optimisation opportunities: workload scheduling, spot instance strategies for training workloads, model distillation to reduce inference cost, and right-sizing of GPU clusters.
• Create business cases and solution specifications for AI platform investments and governance processes, including CTO and architecture approvals.
• Collaborate with the FinOps capability within the CDAIO COO to align AI platform costs to agreed budget envelopes and ensure spend anomalies are detected and escalated proactively 

Platform Observability and SLA Engineering

• Define, implement, and own AI-specific SLAs and OLAs covering inference latency, platform availability, token throughput, API gateway response times, and model serving reliability, with explicit targets agreed with each business unit technology lead.
• Implement and maintain AI platform observability tooling (e.g. Prometheus, Grafana, Datadog, Databricks Lakehouse Monitoring, or equivalent) providing real-time visibility of platform health, model drift alerts, and capacity utilisation.
• Design and operate incident management processes for AI platform failures: on-call runbooks, escalation paths, post-incident reviews, and root-cause remediation, ensuring minimal disruption to live AI projects across Absa's footprint.
• Lead service improvement initiatives, translating performance data into platform enhancement programmes and continuously reducing mean time to recovery (MTTR) across the platform estate.
• Own the release and change management process for AI platform components, including change governance, cutover management, and operational readiness sign-off in alignment with Absa's Group Technology change framework.
• Use production performance monitoring and customer data to inform technical design and implementation decisions; leverage systems and processes to measure, monitor, and manage platform performance bank-wide

AI Security Architecture and Zero Trust

• Design and implement zero-trust security architecture for AI platform APIs and services such as OAuth 2.0 / OIDC integration, JWT/JWE/JWS token management, role-based access control (RBAC), and attribute-based access control (ABAC) for AI workloads.
• Implement prompt injection prevention, output filtering, and data exfiltration controls at the AI Gateway layer, protecting data confidentiality for all LLM and agentic AI interactions across business units.
• Design and enforce data residency and sovereignty controls for AI platform deployments across Absa's operating countries, ensuring compliance with country-specific data localisation requirements and cross-border data transfer restrictions.
• Conduct and maintain AI-specific threat models in collaboration with the Chief Information Security Office, covering third-party AI vendor risks (Databricks, AWS, Microsoft, Hugging Face), model supply chain integrity, and adversarial ML attack vectors.
• Apply and maintain all Group risk, governance, compliance, and regulatory standards and frameworks; hold accountability for all risk associated with AI platform engineering decision-making.
• Update, develop, and maintain all platform documentation in accordance with organisational technical standards and risk and governance frameworks.

People, Capability and Agile Delivery

• Lead and develop a team of AI Platform Engineers, establishing clear performance objectives, providing regular coaching and feedback, and building a high-performance, self-directed squad aligned to agile delivery practices.
• Cascade platform direction across the team; ensure alignment on platform strategy, performance objectives, and delivery priorities. Assume end-to-end accountability for the right people in the right teams to deliver the platform strategy.
• Leverage coaching techniques across all squad-related activity to drive higher-quality design and deployment of AI platform services.
• Maintain comprehensive technical documentation, architectural decision records (ADRs), and operational runbooks for all platform components, ensuring service continuity is independent of individual staffing changes and contractor dependencies are actively mitigated.
• Conduct peer reviews, testing, and problem-solving within and across the broader CDAIO engineering community; identify and develop needed skills in self and others.
• Support the AI Embedment and Training capability in developing platform onboarding materials and self-service guides to accelerate business unit adoption of AI platform services.
• Proactively lead agile practices, remove barriers to success, and ensure seamless delivery in a continuously changing environment.

Qualifications And Experience
 Education/ Qualification:

• Postgraduate degree in a quantitative discipline such as Computer Science, Data Science, Mathematics, Statistics, Engineering, or equivalent ((Masters-essential or PhD-advantageous).
• Certification in:
• Cloud - AWS Solutions Architect Professional, AWS Machine Learning Specialty, or Microsoft Azure AI Engineer Associate).
• FinOps - FinOps Foundation Certified Practitioner (FOCP) or equivalent AI cost governance credential.
• Security Certification - Certified Cloud Security Professional (CCSP) or AWS Security Specialty.
• IaC Certification - HashiCorp Terraform Associate or equivalent infrastructure-as-code credential.

 Work Experience

• 5-8 years of progressive leadership experience in Cloud AI Platform Engineering, with production experience managing multi-cloud AI platform stacks across at least two of: AWS Bedrock/SageMaker, Databricks AI, Microsoft Azure AI Foundry, or Hugging Face enterprise deployments.
• 2–3-year experience in the following:
• AI FinOps and Cost Governance: Demonstrated ownership of AI compute cost models and FinOps reporting in a multi-BU or multi-cloud environment, with evidence of cost optimisation outcomes.
• AI Security Architecture: Designing and implementing zero-trust AI security (OAuth/OIDC, JWT), prompt injection controls, data residency compliance in a regulated environment.
• Agentic AI Infrastructure: Production design of agent orchestration infrastructure (such as LangGraph, AutoGen, Foundry Agent Service, Bedrock Agents), tool-calling APIs, and agent state management.
• Platform Observability: Operating AI-specific observability tooling for inference latency, drift alerting, and capacity management (such as Prometheus, Grafana, Datadog, or Lakehouse Monitoring).
• Infrastructure-as-Code: Terraform, Pulumi, or equivalent for multi-cloud, multi-region AI infrastructure deployments; CI/CD pipeline design for platform components.
• Regulated Industry: AI platform engineering in financial services or a similarly regulated sector with model risk governance and change management obligations. 
• Regulated Industry: AI platform engineering in financial services or a similarly regulated sector with model risk governance and change management obligations
• Advantageous:
• People leadership: Leading or mentoring a team of platform or infrastructure engineers in an agile delivery environment.
• Pan-African Deployments: Delivering AI platform services across multiple African jurisdictions with awareness of data localisation and cross-border data transfer requirements.

Knowledge And Skills

• Multi-Cloud AI Platform Architecture: Expert design and operation of AWS Bedrock, Databricks AI, Azure AI Foundry, and Hugging Face in enterprise production environments across multiple business units and geographies.
• Agentic AI Infrastructure: Practical production knowledge of agent orchestration frameworks (LangGraph, AutoGen, Foundry Agent Service, Bedrock Agents), tool-calling API design, agent memory architecture, and multi-agent coordination patterns.
• AI FinOps and Cost Management: Chargeback and showback model design; DBU and token cost attribution; provisioned throughput versus on-demand optimisation; GPU cluster cost management; spend anomaly detection and FinOps dashboarding.
• AI Security and Zero Trust: OAuth 2.0, OIDC, JWT/JWE/JWS; RBAC and ABAC for AI workloads; prompt injection prevention; data exfiltration controls at the Gateway layer; AI threat modelling and data residency compliance.
• Infrastructure-as-Code: Terraform, Pulumi, or AWS CDK for multi-cloud AI infrastructure; CI/CD pipeline design for platform components; container orchestration using Docker, Kubernetes, and Helm.
• Platform Observability: Prometheus, Grafana, Datadog, OpenTelemetry, and Databricks Lakehouse Monitoring; custom metric design for AI workload health including inference latency, token throughput, and model drift.
• Cloud-Agnostic Model Serving: ONNX, BentoML, Triton Inference Server; containerised model deployment patterns for portability across AWS, Azure, and Databricks environments.
• MLOps Tooling: Working knowledge of MLflow, Kubeflow, Airflow, and CI/CD for ML, sufficient to collaborate effectively with AI Solution Engineers on model deployment and lifecycle management
• GPU and HPC Architecture: On-demand GPU cluster management; spot instance strategies; high-performance compute cost optimisation for large-scale model training and fine-tuning workloads.
• Enterprise Risk and Governance: Absa Enterprise Wide Risk Management Framework; Group Architecture standards; AI Responsible Use Policy; POPIA; country-specific data localisation requirements across Absa's ten operating countries.
• Agile Delivery: Sprint planning, backlog management, and continuous delivery practices in a self-directed squad environment; experience removing delivery barriers in a fast-moving, multi-stakeholder context.

Education

• Bachelor's Degree: Information Technology

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Job Summary

• Absa Group’s Chief Data Analytics and Applied AI Office (CDAIO) requires an experienced and technically capable AI Platform Engineer (Cloud) to support the design, deployment, operation, and continuous improvement of the multi-cloud infrastructure powering the bank’s enterprise AI capability.
• The role will contribute to the delivery of secure, scalable, reliable, and cost-effective AI platform services across multiple business units and countries. The platform supports AI use cases across Corporate and Investment Banking (CIB), Personal and Private Banking (PPB), Business Banking (BB), and Absa Regional Operations (AR).
• The successful candidate will work across technologies such as AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, Kubernetes, and GPU-based infrastructure. The role requires practical experience in cloud platform engineering, infrastructure-as-code, AI workload deployment, platform observability, cloud cost optimisation, security controls, and agentic AI infrastructure.
• The role includes applying critical thinking, design thinking, and problem-solving skills within an agile engineering environment to address complex platform challenges. The AI Platform Engineer will work closely with senior engineers, architects, security teams, FinOps specialists, and AI Solution Engineers to deliver high-quality platform services in line with Absa’s architecture, risk, security, and responsible AI requirements.
• The successful candidate will take accountability for assigned platform components and services while contributing to the broader performance, resilience, and user experience of the enterprise AI platform.

Job Description
Key Focus Areas

• AI Platform Engineering and Architecture - Support the design, deployment, and operation of enterprise-grade, multi-cloud AI infrastructure across AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, and GPU environments.
• AI FinOps and Compute Cost Optimisation - Monitor AI infrastructure consumption, support cost allocation and reporting, and identify opportunities to optimise token usage, Databricks consumption, provisioned throughput, and GPU utilisation.
• Platform Observability and Reliability - Implement and maintain monitoring, alerting, dashboards, and operational processes to ensure the availability, performance, and reliability of production AI platform services.
• AI Security and Zero-Trust Controls - Implement security controls for AI platform APIs, model endpoints, data pipelines, and agentic AI services in line with Absa’s security architecture and regulatory requirements.
• Agentic AI Infrastructure - Support the deployment and operation of infrastructure enabling AI agents, tool-calling services, autonomous workflows, agent memory, and orchestration frameworks.
• Agile Engineering and Collaboration - Deliver platform enhancements through agile practices while collaborating with engineers, architects, business units, security teams, risk stakeholders, and third-party technology providers.

Accountabilities
Platform Engineering and Architecture

• Support the design, deployment, configuration, and operation of Absa’s multi-cloud AI platform stack, including AWS Bedrock, Databricks AI, Microsoft Azure AI Foundry, Hugging Face, and GPU clusters.
• Build and maintain reusable platform components such as AI Gateway configurations, model serving environments, vector databases, API integrations, data pipelines, and containerised workloads.
• Develop and maintain infrastructure-as-code using technologies such as Terraform, Pulumi, AWS CDK, or equivalent tools.
• Contribute to repeatable and auditable infrastructure deployments across multiple cloud environments, regions, and operating countries.
• Configure and support agentic AI infrastructure, including orchestration environments, tool-calling APIs, agent memory, state management, and integration with enterprise systems.
• Implement cloud-agnostic model serving patterns that improve workload portability across AWS, Azure, Databricks, and Kubernetes-based environments.
• Support Kubernetes-based AI workloads using Docker, Kubernetes, and Helm.
• Assist with the evaluation and implementation of new platform technologies, services, and engineering patterns.
• Participate in architectural reviews, technical design sessions, peer reviews, and platform improvement initiatives.
• Create and maintain architectural diagrams, configuration documentation, operational procedures, and technical standards.
• Take accountability for the quality, performance, and operational readiness of assigned platform components.
• Escalate complex architectural, security, capacity, and operational risks to senior engineers and platform leadership.

AI FinOps and Compute Cost Optimisation

• Monitor and analyse AI platform consumption across Databricks, AWS, Azure, GPU infrastructure, and third-party services.
• Support the development and maintenance of chargeback and showback frameworks for business units and individual AI use cases.
• Assist with cost attribution for Databricks DBU consumption, AWS Bedrock token usage, Azure AI Foundry provisioned throughput, and GPU workloads.
• Develop and maintain FinOps dashboards and cost reports using tools such as AWS Cost Explorer, Databricks System Tables, Azure Cost Management, and cloud-native monitoring services.
• Contribute to monthly cost-per-use-case reporting for Finance, platform leadership, and business unit stakeholders.
• Identify opportunities to optimise AI compute costs through workload scheduling, infrastructure right-sizing, token usage controls, caching, spot instances, and efficient model selection.
• Support assessments of provisioned throughput versus on-demand consumption for production AI workloads.
• Monitor spend anomalies and escalate unexpected usage, capacity, or budget risks.
• Provide technical input into business cases and investment proposals for AI platform services.
• Work closely with FinOps specialists and senior platform engineers to ensure infrastructure consumption remains within agreed budget parameters.

Platform Observability and SLA Engineering

• Implement and maintain observability tooling for AI platform infrastructure and production AI services.

Build dashboards and alerts covering:

• Inference latency
• Platform availability
• Token throughput
• API gateway response times
• Model endpoint health
• GPU and compute utilisation
• Databricks workload performance
• Vector database performance
• Capacity utilisation
• Model drift indicators
• Use tools such as Prometheus, Grafana, Datadog, OpenTelemetry, Databricks Lakehouse Monitoring, or equivalent technologies.
• Support the implementation and monitoring of AI-specific service-level agreements and operational-level agreements.
• Participate in incident response, troubleshooting, root-cause analysis, and post-incident reviews for AI platform failures.
• Develop and maintain operational runbooks, support procedures, escalation paths, and recovery documentation.
• Investigate platform performance issues and implement corrective or preventative actions.
• Support release, change, and configuration management processes for AI platform components.
• Conduct technical validation and operational readiness checks before platform changes are released into production.
• Use performance and usage data to recommend improvements to platform scalability, resilience, reliability, and cost efficiency.
• Contribute to initiatives focused on reducing incident volumes and mean time to recovery.

AI Security Architecture and Zero Trust

• Implement zero-trust security controls for AI platform APIs, services, model endpoints, and agentic AI workloads.

Configure and maintain authentication and authorisation controls using:

• OAuth 2.0
• OpenID Connect
• JWT, JWE, and JWS
• Role-based access control
• Attribute-based access control
• Managed identities and service principals
• Support the implementation of prompt injection prevention, output filtering, content controls, and data loss prevention mechanisms at the AI Gateway layer.
• Implement controls to reduce the risk of unauthorised access, data exfiltration, insecure tool-calling, and excessive agent permissions.
• Support data residency and sovereignty controls across Absa’s operating countries.
• Work with architecture, security, risk, and legal stakeholders to ensure AI workloads comply with applicable data localisation and cross-border transfer requirements.
• Contribute to AI-specific threat modelling covering model endpoints, agentic workflows, third-party AI providers, model supply chains, APIs, vector stores, and adversarial machine learning risks.
• Remediate identified security vulnerabilities and configuration risks within agreed timelines.
• Maintain platform documentation and evidence required for security reviews, audits, architecture approvals, and risk governance processes.
• Apply Absa’s Enterprise-Wide Risk Management Framework, Group Architecture standards, information security requirements, and AI Responsible Use Policy in all engineering activities.

Agentic AI Infrastructure

• Support the deployment and operation of agent orchestration technologies such as LangGraph, Microsoft Azure AI Foundry Agent Service, Amazon Bedrock Agents, AutoGen, or equivalent frameworks.
• Configure infrastructure for agent tools, APIs, memory services, vector stores, workflow engines, and enterprise system integrations.
• Implement secure tool-calling patterns, including identity propagation, permission controls, audit logging, timeout management, and failure handling.
• Support agent state management, session persistence, memory controls, and multi-agent communication patterns.
• Implement monitoring and tracing for agent execution paths, tool calls, latency, errors, and resource consumption.
• Work with AI Solution Engineers to move agentic AI solutions from development into controlled, production-ready environments.
• Contribute to platform standards for agent testing, deployment, monitoring, rollback, and lifecycle management.
• Investigate and resolve infrastructure issues affecting the performance, security, or reliability of agentic AI workloads.

Agile Delivery and Capability Development

• Participate actively in sprint planning, backlog refinement, daily stand-ups, technical demonstrations, and retrospectives.
• Estimate engineering effort and deliver assigned platform features within agreed timelines and quality standards.
• Collaborate with platform engineers, cloud engineers, AI Solution Engineers, architects, security specialists, data engineers, and business unit technology teams.
• Participate in code reviews, infrastructure reviews, testing, troubleshooting, and technical problem-solving.
• Contribute to platform engineering standards, reusable templates, automation libraries, and delivery accelerators.
• Maintain comprehensive technical documentation, architectural decision records, deployment guides, and operational runbooks.
• Share technical knowledge and provide guidance to junior engineers and other members of the engineering community.
• Support the development of platform onboarding materials, self-service documentation, and user guides for business unit technology teams.
• Proactively identify technical dependencies, delivery risks, and operational barriers and escalate these appropriately.
• Remain current with developments in cloud AI platforms, agentic AI, MLOps, FinOps, AI security, and platform engineering.

Qualifications And Experience
Education and Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Data Science, Mathematics, Statistics, Engineering, or a related quantitative discipline is essential.
• A postgraduate qualification is advantageous.
• Relevant practical experience may be considered where supported by a strong record of cloud and platform engineering delivery.

Advantageous Certifications
One or more of the following certifications would be advantageous:
Cloud

• AWS Certified Solutions Architect
• AWS Certified Machine Learning Engineer
• Microsoft Certified: Azure AI Engineer Associate
• Microsoft Certified: Azure Solutions Architect Expert
• Databricks Certified Data Engineer or Machine Learning certification

Infrastructure-as-Code

• HashiCorp Certified: Terraform Associate
• Equivalent Terraform, Pulumi, or cloud infrastructure certification

FinOps

• FinOps Certified Practitioner
• Equivalent cloud cost management or financial operations certification

Security

• Certified Cloud Security Professional
• AWS Certified Security
• Microsoft Security, Compliance, and Identity certification
• Equivalent cloud or cybersecurity certification

Work Experience

• Approximately 4 to 6 years of relevant experience in cloud engineering, platform engineering, DevOps, MLOps, infrastructure engineering, or AI platform engineering.
• At least 2 years of practical experience supporting cloud-based data, machine learning, generative AI, or AI platform workloads in a production environment.
• Production experience with at least two of the following:
• AWS Bedrock or Amazon SageMaker
• Databricks
• Microsoft Azure AI Foundry or Azure Machine Learning
• Hugging Face
• Kubernetes-based model serving
• Practical infrastructure-as-code experience using Terraform, Pulumi, AWS CDK, or an equivalent technology.
• Experience building or supporting CI/CD pipelines for cloud infrastructure, platform components, data services, or machine learning workloads.
• Experience with Docker, Kubernetes, Helm, APIs, identity integration, and cloud-native platform services.
• Experience implementing monitoring, dashboards, alerts, and operational support processes for production platforms.
• Working knowledge of cloud cost management, cost allocation, capacity monitoring, and infrastructure optimisation.
• Experience applying cloud security controls, identity and access management, secrets management, and secure API integration.
• Experience working within enterprise risk, architecture, security, and change management processes.
• Experience in financial services, telecommunications, healthcare, insurance, or another regulated industry is advantageous.

Knowledge And Skills

• Multi-Cloud AI Platform Engineering - Practical knowledge of designing, deploying, and supporting AI services across AWS, Microsoft Azure, Databricks, Hugging Face, or Kubernetes-based environments.
• Agentic AI Infrastructure - Working knowledge of agent orchestration frameworks, tool-calling API patterns, agent memory, state management, tracing, and multi-agent workflows.
• AI FinOps and Cost Management - Knowledge of cloud consumption models, token-based pricing, Databricks DBUs, provisioned throughput, GPU utilisation, chargeback and showback reporting, and spend anomaly detection.
• AI Security and Zero Trust - Working knowledge of OAuth 2.0, OIDC, JWT, RBAC, ABAC, API security, managed identities, secrets management, prompt injection controls, data loss prevention, and secure agent tool access.
• Infrastructure-as-Code - Strong practical experience with Terraform, Pulumi, AWS CDK, or equivalent infrastructure automation technologies.
• Containerisation and Orchestration - Experience with Docker, Kubernetes, Helm, container registries, workload scheduling, resource allocation, and production container operations.
• Platform Observability - Experience with Prometheus, Grafana, Datadog, OpenTelemetry, cloud-native monitoring tools, or Databricks Lakehouse Monitoring.
• Cloud-Agnostic Model Serving - Working knowledge of containerised model deployment and serving technologies such as ONNX, BentoML, Triton Inference Server, Kubernetes, or equivalent frameworks.
• MLOps Tooling - Working knowledge of MLflow, Kubeflow, Airflow, model registries, feature stores, automated testing, and CI/CD for machine learning workloads.
• GPU Infrastructure - Understanding of GPU workload deployment, capacity management, right-sizing, spot instance strategies, and cost optimisation for model training and inference.
• Enterprise Risk and Governance - Working knowledge of information security, technology risk, architecture governance, responsible AI, privacy, data residency, and change management requirements within a regulated environment.
• Agile Delivery - Experience working in agile engineering teams using sprint planning, backlog management, iterative delivery, peer review, testing, and continuous improvement practices.

Education

• Bachelor's Degree: Information Technology