AI Engineering Manager at Kifiya Financial Technology

About the Role

• The AI Engineering Manager leads the engineering team responsible for designing, building, and operationalizing scalable AI/ML systems within the Intelligent Data Decisioning (IDD) ecosystem. Combining hands-on machine learning systems expertise with technical leadership, the role ensures that models developed by Data Science teams are production-ready, governed, monitored, and delivering measurable business value.
• The incumbent owns the MLOps architecture, automation frameworks, and AI service pipelines that enable real-time intelligent decisioning across credit, analytics, and risk domains, working closely with Platform Engineering, Data Engineering, and Solutions Architecture teams to ensure seamless model deployment, performance optimization, and compliance across the full AI lifecycle.

What You’ll Do

• Lead, mentor, and develop a team of AI/ML Engineers and MLOps specialists.
• Translate IDD’s AI strategy into executable engineering roadmaps and measurable outcomes.
• Manage sprint planning, performance tracking, and delivery of production-grade AI components.
• Foster a culture of innovation, collaboration, and technical excellence.
• Partner with Data Science leadership to align modeling initiatives with infrastructure capabilities.
• Oversee the end-to-end AI engineering lifecycle , model packaging, deployment, monitoring, and retraining.
• Design and implement MLOps pipelines for reproducibility, version control, model registry, and CI/CD integration.
• Enable real-time inference and low-latency model serving via APIs and streaming services.
• Implement Champion–Challenger frameworks, A/B testing, and automated model retraining workflows.
• Collaborate with Data Scientists to transition prototypes into reliable, scalable production systems.
• Architect modular, containerized AI microservices integrated with the broader IDD data platform.
• Ensure seamless interoperability between AI systems, feature stores, and cloud data services (AWS S3, Databricks, EMR, Aurora).
• Partner with Platform Engineering to ensure compute, storage, and networking configurations support high-throughput model workloads.
• Evaluate emerging AI frameworks, serving technologies, and vector databases to enhance the stack.
• Establish model observability frameworks for drift detection, bias monitoring, and performance degradation alerts.
• Implement governance standards for explainability, traceability, and ethical AI practices.
• Maintain documentation, lineage tracking, and audit readiness for all production models.
• Ensure compliance with enterprise data privacy and regulatory requirements.
• Work with Platform, Data, and Credit Risk teams to integrate model outcomes into decision engines and operational systems.
• Partner with Business and Analytics teams to ensure AI systems deliver measurable value and insights.
• Liaise with InfoSec to align AI workloads with data protection and infrastructure policies.
• Contribute to the evolution of the IDD AI platform roadmap in alignment with enterprise strategy.

What You’ll Bring

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or Artificial Intelligence.
• 7–10+ years of experience in AI/ML engineering, data platform development, or related roles.
• 2–4 years in a team lead or managerial position overseeing AI/ML deployment.
• Proven experience implementing MLOps and production-grade AI pipelines at scale.
• Hands-on experience with machine learning systems engineering and AI platform design.
• Experience with MLOps frameworks such as MLflow, Kubeflow, or SageMaker.
• Proficiency in Python, SQL, and ML libraries (TensorFlow, PyTorch, Scikit-Learn, XGBoost).
• Strong knowledge of data pipelines and workflow orchestration (Airflow, Prefect, or similar).
• Experience deploying models on AWS cloud environments (EKS, Lambda, API Gateway, ECS).
• Familiarity with feature stores, model registries, and model-as-a-service APIs.
• Strong architectural mindset with experience in scaling real-time and batch inference systems.
• Excellent communication, stakeholder engagement, and leadership skills.
• Experience in fintech, banking, or other data-driven environments is preferred.
• Familiarity with credit risk modeling, decision engines, or intelligent automation is a plus.