Senior AI Engineer at Kifiya Financial Technology

About the Role

• The Senior AI Engineer is responsible for designing, developing, and operationalizing machine learning models and intelligent systems that power IDD’s decisioning capabilities.
• This role bridges the gap between Data Science experimentation and production-grade AI systems, ensuring models are deployed, monitored, and scaled effectively within IDD’s cloud and on-prem environments.
• The engineer will focus on building automation pipelines (MLOps), model APIs, and real-time decisioning frameworks that directly support business-critical use cases in risk, credit, and analytics, thriving at the intersection of AI, software engineering, and data infrastructure with a passion for building robust and scalable systems.

What You’ll Do

• Build and deploy machine learning models into production using modern frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost).
• Collaborate with Data Scientists to transform prototypes into efficient, maintainable, and scalable applications.
• Develop APIs and microservices for model inference and decision automation.
• Optimize model performance and resource utilization for low-latency inference.
• Implement versioning, packaging, and containerization standards for ML models.
• Develop and maintain CI/CD pipelines for model deployment, retraining, and monitoring.
• Use MLOps tools such as MLflow, Kubeflow, SageMaker, or Airflow for automation.
• Implement model tracking, performance dashboards, and automated drift detection.
• Build and maintain feature stores and model registries integrated with the IDD platform.
• Integrate AI models into data pipelines, APIs, and decision engines (batch and real-time).
• Collaborate with Platform and Data Engineering teams on infrastructure design (AWS, Databricks, EMR, Aurora, S3).
• Develop robust data ingestion, preprocessing, and transformation pipelines for ML workloads.
• Support event-driven and streaming model architectures using Kafka or Kinesis.
• Build observability into all AI components, monitor drift, bias, and performance degradation.
• Ensure compliance with governance, explainability, and data privacy standards.
• Maintain documentation, model lineage, and reproducibility for all deployed systems.
• Work closely with Data Scientists, Engineers, and Product teams to operationalize AI solutions.
• Contribute to reusable components, internal libraries, and best-practice templates.
• Participate in code reviews, design sessions, and architecture discussions.
• Mentor junior AI Engineers and contribute to a culture of technical excellence.

What You’ll Bring

• Bachelor’s or Master’s in Computer Science, Artificial Intelligence, or a related field.
• 5–8+ years of experience in AI/ML engineering, software engineering, or data science.
• Proven experience deploying and maintaining ML models in production environments.
• Demonstrated understanding of end-to-end model lifecycle (training → serving → monitoring → retraining).
• Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost).
• Practical experience with MLOps tools (MLflow, SageMaker, Kubeflow, Airflow).
• Knowledge of CI/CD, Docker, Kubernetes, and API development (FastAPI, Flask).
• Experience working with AWS cloud (EKS, Lambda, S3, Aurora, CloudWatch).
• Familiarity with data engineering principles and ETL workflows.
• Solid understanding of model observability, monitoring, and drift detection.
• Analytical, detail-oriented, and capable of balancing research with production delivery.
• Experience working in cross-functional engineering teams using Agile/Scrum methodologies.
• Experience with data-driven fintech, credit risk, or analytics-based decision systems is advantageous.