Submit Cvs – New Recruitment Co-operative Bank of Kenya
Full-Time
The Co-operative Bank of Kenya Limited is incorporated in Kenya under the Company Act and is also licensed to do the business of banking under the Banking Act. The Bank was initially registered under the Co-operative Societies Act at the point of founding in 1965. This status was retained up to and until June 27th 2008 when the Banks Special General Meeti AI Integration Engineer The Role Specifically, the successful jobholder will be required to: Design, develop, test and maintain APIs, events, messages, connectors, batch interfaces and secure tool interfaces that enable approved AI models, agents and assistants to retrieve trusted data and perform authorised actions in enterprise systems. Implement integrations through the Bank’s approved API gateway, integration platform and messaging infrastructure using appropriate REST, SOAP, GraphQL, message-queue, event-streaming and batch patterns in line with enterprise integration standards. Translate interface requirements into clear contracts, schemas, mappings, error handling, reconciliation and service-level requirements. Apply security controls including authentication, authorisation, encryption, secrets handling, input validation, throttling, data masking, logging and auditability, with human-in-the-loop controls for sensitive or high-impact actions performed by AI agents. Build resilient integrations with idempotency, retry, timeout, circuit-breaker, queueing and recovery patterns appropriate to the service criticality. Collaborate with system owners, Application Engineers, Data Engineers, Architects, ICT Security and QA to deliver the real-time and batch data feeds required by AI models and decisioning services, coordinate dependencies, and complete end-to-end and regression testing. Manage integration releases through the Bank’s change-management process, coordinating deployment and production handover with system owners and delivery teams. Monitor integration health, performance and reliability; troubleshoot failures; resolve incidents and defects within agreed service levels; and address capacity, resilience and technical-debt concerns. Contribute to reusable integration patterns and API standards in collaboration with the AI & Data Architect and Enterprise Architecture. Maintain interface catalogues, API specifications, schemas, mapping documents, interface agreements, operational procedures, release artefacts and production-support documentation for internal system owners and third-party providers. Skills, Competencies and Experience The successful candidate will be required to have the following skills and competencies: A bachelor’s degree in Computer Science, Software Engineering, Information Technology or a related field. Professional certification in integration platforms, API management, cloud or software engineering is an added advantage. At least four (4) years of experience in systems integration, API engineering, middleware, messaging or backend software engineering. Demonstrated hands-on experience integrating high-availability enterprise systems, including core banking platforms or comparable solutions. Experience in banking, financial services, fintech, telecommunications or payments environments is preferred. REST/JSON, event, messaging and batch integration patterns. API gateways, middleware and service management. Interface contracts, schemas and data mapping. Integration security and secrets management. Resilience, reconciliation and error-handling patterns. Automated integration and contract testing. Monitoring, troubleshooting and production support. Clear technical documentation and stakeholder coordination. Data Engineer The Role Specifically, the successful jobholder will be required to: Design, develop, test and maintain batch, streaming and real-time data pipelines that integrate data from approved source systems into the Bank’s data and AI platforms. Build reusable, well-structured data products and feature sets that support business intelligence, analytics, AI solutions and informed decision-making. Implement data quality controls, reconciliation, metadata, lineage and monitoring mechanisms to ensure data accuracy, completeness, consistency and timeliness. Apply data security, privacy, access controls, encryption and retention requirements in line with the Data Protection Act, 2019, and the Bank’s policies and standards. Optimise data pipelines for performance, reliability, scalability and cost, ensuring effective recovery, auditability and operational resilience. Collaborate with cross-functional teams and business data owners to translate business requirements into fit-for-purpose datasets and data solutions. Integrate automated testing, source control and CI/CD practices into data engineering workflows, supporting deployments across development, testing and production environments. Monitor production pipelines, troubleshoot incidents, resolve defects and maintain technical documentation and operating procedures. Develop data ingestion and processing pipelines for unstructured data, including documents, text and voice, to support generative AI applications and technologies such as vector databases. Skills, Competencies and Experience The successful candidate will be required to have the following skills and competencies: A bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics or a related field. At least four (4) years of relevant experience in data engineering, ETL/ELT development, database engineering or big-data implementation. Hands-on experience building and supporting production data pipelines and curated analytical datasets. Strong proficiency in SQL and Python, Scala or comparable programming languages. Professional certification in data engineering, cloud data platforms, databases or analytics will be an added advantage. Experience in banking, financial services or another regulated data environment will be an added advantage. AI & Data Architect The Role Specifically, the successful jobholder will be required to: Define and maintain the Bank’s AI and data architecture frameworks, standards, principles and strategic roadmaps, ensuring alignment with enterprise architecture. Establish data architecture standards for data modelling, integration, quality, metadata, lineage and master data management to support trusted, governed data products for AI and analytics. Translate business priorities into end-to-end AI and data solution architectures, covering data, models, applications, integration, security, monitoring and support. Develop reusable architecture patterns for machine learning, generative AI and agentic AI, incorporating appropriate governance, safeguards, monitoring and human oversight. Evaluate AI technologies, platforms, models and vendors, recommending fit-for-purpose solutions based on business value, cost, security, scalability, resilience and interoperability. Define standards for AI model deployment, monitoring and lifecycle management, including MLOps and LLMOps, in collaboration with technology, data, security and infrastructure teams. Lead architecture reviews, document design decisions and risks, and ensure compliance with the Bank’s architecture governance frameworks and technical standards. Collaborate with cross-functional teams to resolve design dependencies and ensure solutions meet business, operational, security, privacy, performance and regulatory requirements. Maintain AI and data architecture documentation, capability maps, technology standards and transition roadmaps, while providing technical leadership to delivery teams. Guide AI infrastructure capacity, performance and cost optimisation, ensuring scalable, efficient and sustainable AI adoption across the Bank. Skills, Competencies and Experience The successful candidate will be required to have the following skills and competencies: A bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science or a related field. A relevant master’s degree is an added advantage. Professional certification in enterprise architecture, cloud architecture, data architecture or AI architecture is an added advantage. At least eight (8) years of experience in data, software or solution architecture, including at least three (3) years designing AI, machine learning or advanced analytics solutions. Demonstrated experience designing and delivering enterprise-scale data platforms and production machine learning solutions, including integration of analytical or AI services into operational applications. Relevant data-platform experience may include data lakes, lakehouses or data warehouses. Practical experience designing cloud and hybrid architectures, including containerised deployment and API-led and event-driven integration, with the ability to assess technology choices and implementation trade-offs. Experience in financial services, banking, fintech, telecommunications or another highly regulated industry. AI Application Engineer The Role Specifically, the successful jobholder will be required to: Design, develop, test and maintain AI-enabled application features, services and components for customer-facing and internal solutions, including retrieval-augmented generation, conversational assistants, AI agents, tool-calling workflows, intelligent document processing, recommendations and workflow automation. Translate product, process and user requirements into secure, maintainable technical designs and working software aligned to approved architecture and engineering standards. Take responsibility for integrating models developed by Data Scientists and approved AI services into production applications. Build and test the application APIs, services, user interfaces and workflow integrations required for secure operation and business adoption, maintaining clear separation between application integration and model development responsibilities. Implement and test application-level AI controls appropriate to the approved use case and the Bank’s responsible AI standards. Apply prompt engineering, automated and human evaluation, guardrails, human-in-the-loop controls, fallback behaviour, audit trails, user feedback and explainability features where relevant. Evaluate and document AI output accuracy, safety and consistency, and address identified application-level issues. Write clean, reusable and well-tested code; participate in peer reviews; address defects, vulnerabilities, performance issues and technical debt. Implement application monitoring for availability, performance, usage, model interaction, cost and business outcomes. Use the resulting metrics to identify and address reliability, latency and cost issues within assigned application components. Coordinate with Integration and Platform Engineers on controlled production releases and issues requiring changes outside those components. Coordinate application engineering requirements, dependencies and issue resolution with Product Owners, Data Scientists, Data Engineers, Architects, Integration Engineers, QA, Security and business teams. Deliver the assigned application engineering contributions within cross-functional squads from discovery through adoption. Maintain technical documentation, deployment artefacts and support procedures for assigned application components. Investigate and resolve production issues within those components, coordinating with other teams where required. Develop and maintain reusable AI components, libraries and engineering patterns that support consistent delivery across squads. Skills, Competencies and Experience The successful candidate will be required to have the following skills and competencies: A bachelor’s degree in Computer Science, Software Engineering, Information Technology or a related field. Relevant professional certification in software engineering, cloud development, DevSecOps, AI/ML engineering or generative AI is an added advantage. At least five (5) years of experience in software or application engineering, including delivery of production systems and integrations, with demonstrated experience building applications that use AI, machine learning or generative AI. Demonstrated experience with APIs, modern application frameworks, automated testing and secure software development. Hands-on experience building AI-enabled applications using LLM application frameworks and APIs, with the ability to select and apply suitable technologies within the Bank’s approved architecture. Demonstrated experience integrating AI/ML or generative AI services into production business applications, including application interfaces and controls for secure and reliable operation. Experience in banking, fintech or other regulated digital environments is an added advantage. AI Risk & Compliance Specialist The Role Specifically, the successful jobholder will be required to: Implement and maintain the Bank’s responsible AI governance framework, policies, standards, controls and approval processes. Maintain an enterprise-wide inventory of AI use cases, models, providers and third-party AI tools, ensuring appropriate ownership, risk classification, approvals and lifecycle monitoring. Conduct AI risk and impact assessments covering data protection, cybersecurity, model risk, bias and fairness, explainability, human oversight, third-party risk and regulatory compliance. Establish risk-based control, testing and evidence requirements, including model validation, bias testing, generative AI safeguards and incident management. Coordinate independent reviews and approvals with Risk, Legal, Compliance, Data Protection, ICT Security, Model Risk and relevant business control owners. Monitor AI risks, control effectiveness, model performance, incidents, complaints and remediation actions, preparing reports for management and Board governance forums. Support investigations and resolution of AI-related incidents, control breaches and performance concerns, ensuring lessons learned inform improved controls and practices. Monitor emerging AI regulations and industry developments, maintain governance documentation, and promote responsible AI awareness through targeted training and guidance. Skills, Competencies and Experience The successful candidate will be required to have the following skills and competencies: A bachelor’s degree in Risk Management, Law, Compliance, Information Systems, Data Science, Computer Science, Statistics or a related field. At least six (6) years of relevant experience in risk management, compliance, audit, privacy, model risk, technology risk, data governance or AI governance. Demonstrated experience designing or implementing governance and control frameworks within a regulated environment. Practical exposure to AI/ML risk, model lifecycle controls, data protection and third-party technology risk will be an added advantage. A relevant postgraduate qualification and professional certification in risk, compliance, privacy, information security, audit, data governance or responsible AI will be an added advantage. AI – QA Automation Engineer The Role The successful jobholder will be expected to: Define and maintain risk-based test strategies and plans for AI-enabled applications, data pipelines, integrations and platform services, with traceability to agreed requirements and acceptance criteria. Build and maintain automated test suites for APIs, user interfaces, data pipelines, integrations, regression, performance, resilience and security-related quality checks. Validate the quality, reliability and safety of AI solution behaviour, including relevant safeguards, against approved requirements. Develop representative test data and scenarios that address relevant business, technical and AI risks, while protecting sensitive and personal data and collaborating with ICT Security and AI Risk and Compliance. Integrate automated quality gates into delivery pipelines and ensure defects, failed controls and release risks are visible before deployment decisions. Coordinate end-to-end, performance, recovery and production-verification testing with technology teams, and support business-led user acceptance testing. Maintain the resulting test evidence in line with audit and governance requirements; business acceptance remains with the designated business owner. Maintain objective test evidence, track defects and verify fixes, working with responsible teams to resolve issues. Report residual risks and provide an independent quality assessment without assuming business or risk approval authority. Review post-release quality with engineering and support teams, and use the findings to improve test coverage, automation and quality practices across AI delivery squads. Qualifications, Skills & Attributes The successful jobholder will be required to possess the following qualifications, skills and attributes: A bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science or a related field. Professional certifications such as ISTQB Foundation Level, Test Automation Engineer, AI Testing, agile testing or an equivalent quality-engineering certification are an added advantage. At least four (4) years of experience in software quality assurance or quality engineering, including at least two (2) years of strong hands-on test-automation experience. Demonstrated experience testing APIs, integrations, digital applications and data-intensive systems. Exposure to AI/ML validation, model testing or data-quality testing is preferred. Test strategy, risk-based testing and requirements traceability. UI, API, integration and data-pipeline automation. Performance, resilience and security-related testing. AI/ML quality concepts, metrics, bias and guardrail testing. CI/CD quality gates and test reporting. Test-data management and privacy controls. Defect management and root-cause analysis. Independent judgement and concise release-risk communication. Data Scientist The Role Specifically, the successful jobholder will be required to: Collaborate with business and product teams to identify priority business challenges, define analytical approaches and establish measurable outcomes. Analyse complex datasets to uncover trends, patterns and opportunities, translating findings into actionable insights and recommendations for business leaders and stakeholders. Develop, train, test and validate statistical, machine learning, generative AI and optimisation models using robust and reproducible methodologies. Establish model performance benchmarks, validation criteria and monitoring frameworks, ensuring fairness, explainability, appropriate human oversight and compliance with the Bank’s responsible AI and model risk requirements. Work with Data Engineers and Application Engineers to deploy models and AI solutions into production using approved MLOps/LLMOps practices. Design and conduct experiments, pilots and post-deployment evaluations to measure business impact and continuously improve model performance. Monitor model effectiveness and business outcomes, identify performance deterioration or data drift, and recommend model recalibration, retraining or retirement where necessary. Maintain comprehensive model documentation, code and validation evidence to support governance, audit and ongoing model management. Contribute reusable analytical assets and share knowledge to strengthen data science and AI capabilities across the Bank. Skills, Competencies and Experience The successful candidate will be required to have the following skills and competencies: A bachelor’s degree in Data Science, Statistics, Mathematics, Actuarial Science, Computer Science, Economics, Engineering, Operations Research or a related quantitative field. At least four (4) years of relevant experience in data science, advanced analytics, machine learning or applied AI. Demonstrated experience taking analytical or machine learning solutions from problem definition through validation to production deployment. Proficiency in Python or R and SQL, with strong knowledge of statistical modelling, machine learning, feature engineering and model evaluation. Experience in credit, fraud, collections, customer analytics, banking operations or a related business domain will be an added advantage. A relevant master’s degree or professional certification in data science, machine learning, cloud computing or AI will be an added advantage AI Data Architect The Role Specifically, the successful jobholder will be required to: Define the target architecture and transition roadmap for the Bank’s enterprise data platforms, including data warehouses, data lakes or lakehouses, real-time streaming capabilities, integration patterns and consumption layers, within the approved enterprise architecture. Design conceptual, logical and physical data models for priority data products, analytical solutions and AI use cases, with clear definitions, ownership and quality requirements. Define architecture patterns for data ingestion, storage, transformation, sharing, retention, archival and deletion across batch, streaming and real-time use cases. Partner with business data owners and Data Governance teams to establish common definitions, metadata, lineage, master and reference data requirements and authoritative sources. Guide Data Engineers in implementing scalable, reusable and governed data products, ensuring traceability from source to consumption, rationalising legacy data stores, reducing unnecessary duplication and defining migration approaches towards the target-state data platform. Ensure data designs incorporate security classification, access control, privacy, segregation, encryption, retention, resilience and regulatory requirements by design, including compliance with the Data Protection Act, 2019. Review data solution designs and proposed changes to source systems for data impact, represent data architecture at the Architecture Review Board, document architecture decisions and exceptions, and ensure identified risks and technical debt have accountable owners and agreed remediation plans. Maintain enterprise data models, data cataloguing requirements and architecture artefacts, and provide technical guidance to delivery squads, Data Engineers, Data Scientists, the AI and Data Architect, business data owners and other stakeholders. Skills, Competencies and Experience The successful candidate will be required to have the following skills and competencies: A bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, Mathematics or a related field. A relevant master’s degree is an added advantage. Professional certification in data management, data architecture, enterprise architecture or cloud data platforms is an added advantage. At least seven (7) years of experience in data management, data engineering, database design, analytics or architecture, including at least three (3) years in data architecture or a senior data modelling role. Demonstrated experience designing enterprise data models and modern analytical data platforms. Experience in banking, financial services or another data-intensive regulated environment is preferred. Conceptual, logical and physical data modelling. Data warehousing, lakehouse, data lake and data product patterns. Metadata, lineage, master and reference data, and data quality. Batch, streaming and real-time data architecture. SQL and relational and non-relational data technologies. Privacy, security, records retention and resilience by design. Data architecture governance and design review. Communication with business data owners and technical teams. Flexibility to work across business and technology teams and to support critical delivery or production activities when required. Commitment to confidentiality, responsible use of data and AI, and compliance with the Bank’s policies and control requirements. Don't Keep Share!: WhatsApp Tweet