About the role

We're looking for a hands-on ML qualified 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, quick 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: solid 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

📌 Machine Learning Engineer Gurugram
🏢 Hero FinCorp
📍 Gurugram