Head of AI & Platform Engineering at Kifiya Financial Technology

About the Role

• The Head of AI & Platform Engineering will lead the design, development, and scalability of Kifiya’s AI and data platforms, ensuring seamless integration of AI/ML capabilities, production-grade systems, and automated infrastructure.
• Overseeing two specialized teams—the Platform Engineering Team, responsible for infrastructure, automation, DevOps, and scalable data systems, and the AI/ML Engineering Team, responsible for AI model deployment, MLOps pipelines, and real-time intelligent systems—the role’s mission is to establish a high-performing, automated, and scalable AI platform ecosystem that drives business growth, operational resilience, and innovation across IDD and the wider enterprise.

What You’ll Do

• Define and execute the AI & Platform Engineering strategy aligned with IDD’s and CDO’s objectives.
• Build and lead a high-performing dual-team structure, fostering collaboration between Platform Engineers and AI/ML Engineers.
• Translate business goals into scalable technical architectures and actionable engineering roadmaps.
• Serve as a bridge between Data Science, Data Engineering, and Credit Risk streams to ensure seamless operationalization of analytics and models.
• Lead the development of cloud-native, containerized, and automated platforms (e.g., AWS, Kubernetes, EKS, Terraform, CI/CD pipelines).
• Drive the modernization of data and compute infrastructure to support advanced analytics, ML workloads, and large-scale data pipelines.
• Oversee platform reliability, performance, monitoring, and cost optimization.
• Ensure security, compliance, and governance are embedded into platform design and operations.
• Oversee the end-to-end AI/ML engineering lifecycle , from model packaging and deployment to monitoring, retraining, and scaling.
• Implement robust MLOps frameworks for model versioning, reproducibility, and real-time inference.
• Collaborate with Data Science teams to transition prototypes into production-grade intelligent systems.
• Drive automation of model retraining, performance tracking, and A/B testing (Champion–Challenger frameworks).
• Partner with Solutions Architecture and Data Engineering teams to ensure seamless interoperability between systems and tools.
• Design modular, API-driven architectures for model serving, feature stores, and AI services.
• Evaluate emerging tools and technologies to continuously evolve the AI and data platform stack.
• Define engineering standards, policies, and documentation practices for AI and platform functions.
• Promote DevSecOps, MLOps, and DataOps best practices across the IDD ecosystem.
• Ensure systems comply with enterprise data governance, security, and privacy frameworks.
• Work closely with the CDO, Chief of IDD, and departmental leads to align infrastructure capabilities with business needs.
• Provide technical advisory support to Data Science, Analytics, and Risk teams for scalable solution design.
• Drive collaboration with IT, InfoSec, and Cloud Infrastructure teams to ensure alignment on enterprise standards.

What You’ll Bring

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, AI/ML, or related field.
• 10+ years of experience in software engineering, data platform management, or AI/ML engineering roles, with at least 5 years in leadership.
• Proven experience building AI platforms, MLOps environments, or cloud-based data ecosystems.
• Hands-on experience with Kubernetes (EKS/GKE), CI/CD, Spark, MLFlow, Airflow, Kafka, or equivalent tools.
• Deep expertise in cloud platforms (AWS, Azure, GCP), Kubernetes, Docker, and infrastructure as code (Terraform, CloudFormation).
• Advanced understanding of AI/ML systems, model deployment pipelines, feature stores, and real-time APIs.
• Excellent understanding of DevOps, MLOps, and automation frameworks.
• Strong architectural mindset with the ability to balance innovation and operational excellence.
• Exceptional communication and stakeholder management skills.
• Familiarity with modern data stacks (e.g., Snowflake, Databricks, StarRocks, Presto, ClickHouse) is a strong advantage.
• Experience in financial services or fintech environments preferred.

Deadline for submission: 18 December 2025.

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