Machine Learning Engineer

About the role

We're looking for a hands-on ML professional who can take models from notebook to production and keep them healthy there. You'll own the deployment and inference stack for our credit risk, underwriting and collections models, and work closely with data scientists, data engineers and business teams to make ML reliable, fast and auditable.

Key responsibilities

• Build and run end-to-end ML pipelines covering training, validation, deployment and retraining
• Deploy models for batch and real-time inference, with attention to latency, throughput and cost
• Manage model versioning, registry and release of models into production
• Set up monitoring for data drift, model drift, performance decay and pipeline failures, with alerting
• Build data pipelines and feature stores that feed models in production
• Work with data scientists on model development, feature engineering and experiment tracking
• Make sure models meet governance, explainability and audit requirements for a regulated lender
• Optimise inference through model packaging, containerisation and scaling

Must-have skills

• Inference and deployment: real-time and batch model serving (FastAPI, Seldon, KServe, SageMaker or Databricks Model Serving), latency optimisation, A/B and shadow deployments
• MLOps tooling: Docker, MLflow or similar for tracking and registry; Kubernetes is a plus
• Data science and ML: hands-on with scikit-learn, XGBoost/LightGBM and model evaluation; comfortable building and debugging models, not just deploying them
• Data engineering: strong Python and SQL, Spark/PySpark, workflow orchestration (Airflow or similar), ETL/ELT design
• ML engineering: clean, testable production code and Git
• Cloud: hands-on with at least one of AWS, Azure or GCP

Good to have

• Banking, NBFC or financial services experience, especially credit risk, underwriting, collections or fraud models
• Databricks and feature store experience
• Experience with model monitoring tools (Evidently, Arize or custom)
• Exposure to LLM/GenAI deployment, RAG or conversational systems
• Knowledge of RBI guidelines on model risk, data privacy and explainability

Qualifications

• B.Tech/M.Tech/MSc in Computer Science, Data Science, Statistics or a related field
• Certifications such as Databricks ML, AWS/Azure ML or Kubernetes are a plus