Job Opportunities at Strathmore University

Basic job summary:

• The role is responsible for leading advanced data science workstreams related to AI model development, benchmarking, safety testing, and applied analytics. will involve implementing data ingestion frameworks,

Duties & Responsibilities:
Data Pipelines and Reporting

• Contributes significantly to the creation of the data architecture in terms of projected and expected data needs, performance and efficiency KPIs.Scopes and stages work into well-defined milestones to avoid monolithic deliverable.
• Go-to expert in are or the codebase. Understands architecture of the entire systems and provides technical advice and weights on the technical decisions that impact whole project.
• Able to successfully design and build end-to-end solutions with guidance from experts in the fields.

Data Science Strategy and Planning

• Contribute to the development and implementation of data science strategies.
• Work with cross-functional teams to identify and prioritize data science requirements.
• Support in recommending and implementing new technologies to enhance data science capabilities.

Data Quality and Governance

• Ensure the accuracy and reliability of data through data profiling, cleansing, and validation.
• Collaborate with data governance teams to establish and maintain data quality standards.
• Acquire data from primary or secondary data sources, filter, and clean data, maintain databases/data systems, and ensure data quality.
• Research on governance trends, best practices and improves on existing implementations. Constantly looking for improvements on the previous iterations.

Advanced Analytics and Modeling

• Model, design, and implement AI algorithms using diverse sources of data.
• Design and implement rigorous evaluation pipelines for AI models including large language models (LLMs), retrieval-augmented systems, and task-specific models.
• Support in the development and maintenance of benchmarking datasets (e.g. agricultural Q&A, edge cases, contextual prompts) to support standardized model assessment.
• Lead technical safety testing of AI advisory systems, including hallucination detection, inappropriate content identification, and escalation logic.
• Support the development and testing of guardrails, disclaimers, and fallback mechanisms for farmer-facing advisory use cases.
• Design and analyse experiments (e.g. A/B testing, persona-based trials) to assess AI output quality, usability, and performance across different contexts.
• Work closely with Data Engineers and MLOps Engineers to ensure AI pipelines are reproducible, auditable, and well-documented.
• Explore, learn, and deliver more complex tasks, including robust scheduled code execution, building frameworks and Apis for the rest of the team and building eventbased data processing

Collaboration and Stakeholder Management

• Collaborate with internal and external stakeholders to gather business requirements and understand analytical needs.
• Provide support and training to end-users on utilizing data science tools and interpreting analytical outputs.
• Support in writing reports, documentations, and publications related to business intelligence.
• Liaise and work effectively with the software development team to ensure all data needs are well addressed in projects.
• Research new and emerging trends in data science to grow skills and facilitate client projects.

Project Management and Follow-up

• Achieve results through follow-up of projects through to completion.
• Monitor project progress, manage priorities, commit to achieving quality outcomes, adhere to documentation procedures, and seek feedback from stakeholders to gauge satisfaction.

Minimum Academic Qualifications:

• Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, Artificial Intelligence, or a closely related quantitative field.

Experience:

• Applicants should possess at least 5 years of progressive experience in data science, advanced analytics, or applied research roles, with demonstrated responsibility for complex analytical or modelling tasks.

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Basic job summary:

• The Senior Data Engineer will be responsible for designing, building, and maintaining data infrastructure and pipelines. l mplementing data ingestion frameworks,

Duties & Responsibilities:
Data Pipeline Design and Implementation

• Design, implement, and maintain robust data ingestion and processing pipelines for heterogeneous data sources, including soil, weather, agronomic, geospatial, and related contextual datasets.
• Develop scalable ETL/ELT workflows to transform raw data into structured, validated, and analytics-ready formats.
• Ensure pipelines support both batch and, where required, near-real-time data processing.
• Implement data versioning and lineage tracking to support reproducibility and auditability.

Cloud-Based Data Infrastructure

• Design and manage cloud-native data architectures, including data lakes, data warehouses, and analytical storage solutions.
• Optimize data storage and processing for performance, cost efficiency, and scalability.
• Support deployment of data pipelines across development, testing, and pilot environments.
• Collaborate with platform teams to ensure infrastructure aligns with DPI principles and interoperability standards.

Data Quality, Governance, and Reliability

• Implement automated data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and consistency.
• Support enforcement of data governance requirements, including access controls, permissions, and audit logging.
• Work with policy and governance partners to ensure technical implementations align with data protection and consent frameworks.
• Proactively identify and remediate data reliability risks or bottlenecks.

Enablement of AI and LLM-Based Systems

• Prepare and serve data in formats optimized for AI and LLM-based advisory systems, including retrieval-augmented generation (RAG) pipelines and structured knowledge services.
• Support model evaluation, benchmarking, and experimentation workflows.

MLOps Support and Operational Readiness

• Contribute to MLOps workflows by supporting data versioning, pipeline automation, and integration with model deployment and evaluation processes.
• Implement monitoring and logging for data pipelines to support observability and issue diagnosis.
• Support reproducible experimentation through consistent data environments and pipeline automation.

Documentation, Collaboration, and Delivery

• Produce clear technical documentation covering data architectures, pipeline logic, and operational procedures.

Minimum Academic Qualifications:

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a closely related technical field

Experience:

• Applicants should possess at least 5 years of professional experience in data engineering, with demonstrated responsibility for designing and operating complex data pipelines and data platforms.
• Strong experience designing and implementing data ingestion, transformation, and processing pipelines (ETL/ELT) for large and heterogeneous datasets.
• Proficiency in Python and SQL, and experience with data processing frameworks and tools commonly used in modern data engineering environments

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Basic job summary:

The Senior Software Engineer will be responsible for designing, building, and operationalizing software infrastructure. This role will lead the full-stack development and system integration of backend services, APIs, and data pipelines.

Responsibilities:

Software Development and Design

• Collaborate with the project technical lead and other team members to analyze requirements and design software solutions for AI applications.
• Develop, test, and debug software components for data exchange gateways, and cloud platforms.
• Assist in implementing data management, analytics, and visualization features for AI applications.
• Implement engineering frameworks that enable LLM-based advisory systems, including retrieval-augmented generation (RAG), structured knowledge integration, and prompt orchestration.
• Integrate soil, weather, and agronomic datasets into retrieval and reasoning pipelines to support contextualized and actionable advisory outputs.
• Support experimentation with different GenAI architectures and system configurations in collaboration with data science teams.
• Develop or support frontend and interface components (e.g. dashboards, admin tools, sandbox interfaces) required for internal testing, monitoring, and partner integration.
• Implement technical controls to support data governance requirements, including consent-aware data access, role-based permissions, and audit logging.
• Participate in code reviews and maintain coding standards and best practices.

.Quality Assurance and Testing

• Debug Identify and address any software-related issues, anomalies, or performance bottlenecks.
• Collaborate with the Quality Assurance team to ensure a high-quality optimized code solution.
• Ensure the security and integrity of AI software systems, implementing encryption, authentication, and access control mechanisms as necessary.
• Perform code reviews, testing, and debugging activities to maintain high quality and reliability in software deliverables.
• Ensure secure handling of sensitive or regulated data in line with Kenya’s Data
• Protection Act and project governance frameworks.
• Embed responsible AI considerations into system design, including safeguards, escalation pathways, and human-in-the-loop mechanisms where required.

Documentation and Reporting

• Create and maintain comprehensive repository documentation for software designs, iterations, specifications, and testing procedures.
• Develop standard operating procedures (SOPs) for software MVP development and testing.
• Generate simulation and evaluation software code report before final release version for deployment

Collaboration and Support

• Collaborate with research, data science and engineering teams to meet project timelines and deliverables.
• Provide technical guidance and mentorship to junior software developers, fostering a culture of innovation and continuous learning

Minimum Academic Qualifications:

• Bachelor’s degree or Master’s degree in Computer Science, Software Engineering, Information Systems, Data Science, or a closely related technical field from a recognized institution

Experience:

• Applicants should possess a minimum of 7 years’ experience in software development