Description

 Experience: 9+ Years Overall Experience
Relevant Experience: 7.5+ Years
Education: 15 Years Full-Time Education
Primary Skill: Machine Learning (ML)
Domain: Data Platform / Machine Learning / MLOps

Key Responsibilities
Design and implement end-to-end Machine Learning pipelines
Operationalize ML models using MLflow for tracking, packaging, and deployment
Establish MLOps practices including version control, reproducibility, monitoring, and rollback
Automate model training, evaluation, validation, and deployment workflows
Collaborate with Data Scientists to productionize ML experiments
Manage and optimize cloud infrastructure for scalable ML training and inference
Integrate ML models with APIs, batch jobs, and real-time inference systems
Implement governance, lineage, and audit trails for ML artifacts and pipelines

Required Technical Skills
Strong hands-on experience with MLflow & ML Lifecycle Management
Strong Python programming skills
Experience with Scikit-learn / TensorFlow / PyTorch
Hands-on experience with AWS / Azure / GCP ML services
Understanding of CI/CD & DevOps principles for ML
Experience with Docker & Kubernetes or serverless architectures
Knowledge of Feature Stores, Experiment Tracking & Model Registries

⭐ Good to Have
Knowledge of Vector Databases
Experience with LangChain / LLM Orchestration Tools
Databricks + MLflow integration experience
Exposure to ML monitoring, drift detection, data quality & performance monitoring
Understanding of AI governance, privacy, anonymization, fairness & explainability

Key Skills

Education

Not specified