Vacancies at FinSense Africa

Job Description

As the AI Adoption & Enablement Lead, this role is the primary change agent driving the human adoption of AI across the organization – turning the AI platform’s capabilities into real, everyday productivity gains as part of the organization's multi-year AI Workforce Transformation. The role bridges the AI engineering team and the wider business, translating what is technically possible into what is practical and valuable for teams.

The position requires a blend of technology fluency, communication, training and change management skills. It exists to accelerate safe, responsible AI adoption – cultivating a network of AI champions, sourcing and shepherding high-impact use cases, and embedding AI copilots into daily workflows – so that the organization becomes a genuinely AI-augmented & human-led.

Requirements

Technical Competencies

Adoption Strategy & Planning

• Develop and own the AI adoption and enablement roadmap aligned to the transformation Blueprint, with clear targets for the AI Augmentation Index.

Training Programme Delivery

• Design and run training curricula, workshops, demos and onboarding for AI copilots and tools across business units.

Enablement Content

• Produce playbooks, quick-start guides, prompt libraries, FAQs and success stories that make AI easy to adopt and reuse.

Champions Network

• Build and coordinate a cross-functional AI champions network and community of practice; equip champions to drive adoption locally.

Use-Case Pipeline

• Source, qualify and prioritise AI use cases with business owners and the AI engineering team; track them from idea to adoption.

Adoption Measurement

• Define adoption KPIs, instrument usage tracking with the engineering team, and report progress and impact to leadership and the AI Steering Committee.

Responsible-AI Enablement

• Embed human-in-the-loop, transparency and responsible-AI guidance into all enablement; help users understand controls and escalation paths.

Stakeholder Engagement

• Partner with business-unit leaders, HR/L&D, Risk and Compliance and Internal Communications to land adoption initiatives smoothly.

Feedback Loop

• Gather user feedback and adoption barriers and channel them back to the AI engineering team to improve tools and experience.

External Thought Leadership

• Represent the organization selectively at partner forums and industry events and through content, strengthening thought leadership and the employer brand.

Continuous Improvement

• Stay current on AI adoption best practice and continuously refine enablement approaches.

Education Requirements

• A Bachelor’s degree in a relevant field (Computer Science, Business, Communications or related; a Master’s is an added advantage), with 5+ years in technology adoption, enablement, developer relations, change management or technical training – ideally including AI/ML or digital-transformation programmes.

AI & Technology Fluency

• Strong working understanding of AI/ML and Large Language Models – what they can and cannot do, prompt design, copilots and common enterprise use cases – sufficient to translate capabilities into practical business value (hands-on coding is not required).

Change Management & Adoption

• Proven track record of driving technology adoption or transformation – changing how people work, not just informing them – using recognised change-management approaches.

Training & Facilitation

• Excellent ability to design and deliver engaging training, workshops and demos for technical and non-technical audiences; skilled at producing playbooks and enablement content.

Communication & Influence

• Outstanding communication, storytelling and stakeholder-influencing skills; able to build trust and rally diverse teams around AI initiatives.

Community Building

• Experience building and energising communities of practice, champion networks or developer / user communities.

Measurement & Insight

Ability to define and track adoption metrics (usage, proficiency, impact) and turn insight into action; comfortable with dashboards and simple analytics.

Responsible AI & Domain Awareness

• Awareness of responsible-AI, privacy and compliance principles and good knowledge of the financial-services context; able to advocate safe, ethical AI use.

Certifications

• Change-management (e.g. PROSCI), training / facilitation, or AI/ML foundational certifications are advantageous.

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Job Description

• As an AI Engineer, this hands-on technical role builds, integrates and ships the AI services that power the organization's multi-year AI Workforce Transformation. Working under the direction of the Senior Manager, AI Engineering and the Principal AI Engineers, the role implements components of the AI platform – from orchestration workflows to LLM Gateway integrations – embedding AI into key workflows.
• The position requires solid software-engineering fundamentals and a working knowledge of modern AI/ML tools. It exists to turn architectural designs and standards into reliable, production-ready AI capabilities that elevate operational productivity and augment human work, leveraging unique datasets.
• This is a role that requires the engineer to apply security, privacy and responsible-AI controls in everyday delivery – in line with “AI in the Loop / Human in the Loop” philosophy – escalating risks and ensuring AI is implemented safely, ethically and effectively.

Education & Work Experience

• A Bachelor’s degree in Computer Science, Software Engineering or related field (a Master’s degree in AI/ML or Data Science is a plus), with 2–4 years’ software-engineering experience including hands-on exposure to AI/ML or data-intensive applications.

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Job Purpose

The role holder is responsible for ensuring information systems developed and deployed meet the organization's cybersecurity policies, standards, and requirements, as well as complying with applicable cybersecurity regulations and industry standards.

The role holder will ensure that security requirements are properly captured and embedded within the Software Development Life Cycle (SDLC) for all technology initiatives, secure coding practices are adhered to, and secure software and application configurations are maintained.

The specialist will carry out security testing across all technology stacks (mobile, web applications, APIs/microservices, source code, web servers, containers, servers, databases, virtualization environments, network devices, and connectivity) within assigned scrum teams and projects.

Responsibilities

• Work with scrum and project teams to ensure that security requirements are adequately captured during the requirements analysis phase.
• Provide input into the secure design of information systems architecture throughout the project lifecycle.
• Ensure that access to systems during the project lifecycle by staff, contractors, and vendors is secure and based on the principle of least privilege.
• Enforce the implementation and adoption of minimum security baseline standards across all technologies in use.
• Facilitate the identification of security vulnerabilities by performing or coordinating security assessments, vulnerability assessments, and penetration testing (VAPT).
• Ensure security tools and controls are operating as expected within development and deployment pipelines and review security reports generated from them.
• Report security gaps identified within scrum teams and projects and follow up on remediation in accordance with organizational standards and procedures.
• Identify security violations and incidents during the project lifecycle and coordinate the response process.
• Ensure effective integration of security tools to protect, detect, and respond to attempted intrusions before and during project go-live.
• Collaborate with project teams to ensure user access matrices are properly defined and aligned with established roles and responsibilities.
• Participate in deployment activities and conduct post-implementation reviews (PIR) to ensure security configurations are implemented and identified gaps do not progress into production environments.
• Embed cybersecurity awareness initiatives throughout the project lifecycle, with a focus on secure coding practices.
• Provide scheduled security reports to cybersecurity leadership, project teams, and steering committees on the progress of security workstream activities.

Requirements

Skills and Experience

• Bachelor’s degree in Computer Science, Information Technology, or other STEM-related discipline.
• Master’s degree in Information Security, Cyber Security, or related field will be an added advantage.
• Professional information security certifications such as CISA, CISM, CISSP, CRISC, or Security+, as well as application/security testing certifications such as CSSLP, CEH, OSCP, CPT, GPEN, GWAPT, or eJPT.
• 3+ years of experience in technology roles.
• 1+ years of experience in information security.
• 1+ years of experience in Application Security within Secure SDLC and DevSecOps environments.
• Strong technical expertise in DevSecOps toolchains, including tools such as Ansible, Jenkins, GitLab, Azure DevOps, Trivy, SonarQube, Terraform, Git/version control systems, or similar technologies.
• Familiarity with information security frameworks and standards such as PCI-DSS, ISO 27001, and SABSA.
• Knowledge of API security, container security, and cloud security principles.
• Experience in project implementation and user training.
• Ability to multitask, work effectively under pressure and tight deadlines, influence stakeholders, and operate both independently and within cross-functional teams.
• Strong verbal and written communication skills.
• Strong analytical and problem-solving skills with the ability to collaborate effectively across teams.

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Job Description

As the Principal AI Engineer, this role is a senior technical leader driving the design and delivery of the AI platform that underpins the organization's multi-year AI Workforce
Transformation. The role owns the end-to-end AI architecture – from the AI Orchestration Layer to a unified LLM Gateway – ensuring the platform seamlessly embeds AI across key workflows.

The position requires deep expertise in modern AI/ML platforms and enterprise architecture, operating at a senior level as an individual contributor. It exists to elevate operational productivity through data-centric AI enablement, leveraging unique datasets to build proprietary AI solutions that augment human capabilities.

This is a role that requires the ability to champion compliance, security and responsible AI – upholding strict governance and human-first design in line with “AI in the Loop / Human in the Loop” philosophy – ensuring AI technologies are implemented safely, ethically and effectively to drive a human-led, AI-augmented organization.

Education & Experience

• A Bachelor’s degree in Computer Science, Software Engineering or related field (a Master’s degree in AI/ML or Data Science is an added advantage), with 8+ years in software engineering or architecture – including at least 3–5 years designing AI, data or cloud architectures at scale and leading the technical design of complex AI/ML platforms or datadriven products.

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Job Description

As the Senior Manager, AI Engineering, this role provides enterprise-wide architectural and people leadership for the AI platform and the multi-year AI Workforce Transformation. Beyond owning the end-to-end AI architecture from the AI Orchestration Layer to a unified LLM Gateway, the role sets the organisation-wide AI architecture strategy, standards and governance, and leads the AI Engineering team (Principal AI Engineers, AI Engineers and the AI Adoption & Enablement Lead).

The position requires authoritative expertise in modern AI/ML platforms and enterprise architecture, operating at a principal level. It exists to elevate operational productivity through data-centric AI enablement at scale, leveraging unique datasets to build proprietary AI solutions that augment human capabilities across every domain.

Education & Experience

• A Bachelor’s degree in Computer Science, Software Engineering or related field (a Master’s degree in AI/ML or Data Science is strongly preferred), with 12+ years in software engineering or architecture – including at least 6 years designing and leading AI, data or cloud architectures at scale, with demonstrable enterprise / transformation leadership and peoplemanagement experience.

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Key Responsibilities

• Assess the current state of engineering performance, DevOps maturity, platform reliability, and AI readiness, and establish improvement priorities.
• Lead the design and execution of initiatives that improve software delivery performance, quality, reliability, and developer productivity.
• Provide technical leadership and design authority across engineering, platform, DevOps, and AI enablement initiatives.
• Establish and strengthen CI/CD, testing, observability, release management, and reliability practices aligned with industry best practices.
• Define and implement governed AI enablement frameworks, including security, access controls, auditability, knowledge retrieval, and responsible AI practices.
• Collaborate with engineering, infrastructure, security, risk, compliance, operations, business stakeholders, and external partners to drive aligned execution.
• Develop and monitor engineering performance metrics and continuous improvement initiatives to ensure measurable business outcomes.
• Mentor internal teams and embed sustainable engineering standards, governance, and knowledge transfer to build long-term capability.

Requirements

Experience

• Minimum 7 years' experience in software engineering or platform engineering.
• Minimum 5 years in senior technical leadership or principal engineering roles.
• Demonstrated experience leading enterprise DevOps transformations.
• Experience improving engineering performance using DORA metrics or equivalent frameworks.
• Proven experience designing and implementing CI/CD pipelines.
• Experience leading cloud platform modernization initiatives.
• Experience implementing observability and reliability engineering practices.
• Practical experience delivering enterprise AI solutions with governance and security controls.
• Experience working within regulated or enterprise environments is highly desirable.