Project Role : Data Platform Engineer

Project Role Description : Assists with the data platform blueprint and design, encompassing the relevant data platform components. Collaborates with the Integration Architects and Data Architects to ensure cohesive integration between systems and data models.

Must have skills : Machine Learning (ML)

Good to have skills : NA

Minimum 7.5 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Summary:

As a Data Platform Engineer, you will assist with the data platform blueprint and design, collaborating with Integration Architects and Data Architects to ensure cohesive integration between systems and data models. You will play a crucial role in shaping the data platform components.

Roles and Responsibilities:

Design and implement end-to-end machine learning pipelines, from data preprocessing to model training and deployment.

Operationalize ML models using ML flow for tracking, packaging, and deploying models across environments.

Set up and maintain ML Ops practices, including version control, reproducibility, monitoring, and rollback strategies.

Automate workflows for model training, evaluation, validation, and deployment across AWS, Azure, or GCP.

Collaborate with data scientists to productionize experiments and improve model lifecycle processes.

Manage and optimize cloud infrastructure for scalable ML training and inference.

Integrate models into APIs, batch jobs, or real-time inference systems.

Implement governance, lineage, and audit trails for ML artifacts and pipelines.

Technical Skills:

Strong hands-on experience with ML flow, model deployment, and ML lifecycle management.

Proficiency in Python, scikit-learn, TensorFlow, Py Torch, or similar ML libraries.

Experience working with cloud ML services (e.g., SageMaker, Vertex AI, Azure ML).

Understanding of CI/CD and DevOps principles for ML.

Familiarity with Docker, Kubernetes, or serverless architectures.

Experience with feature stores, experiment tracking, and model registries.

Additional Information:

The candidate should have a minimum of 9 years of overall experience

Knowledge of Vector Databases, Lang Chain, or LLM orchestration tools.

Experience with Databricks ML flow integration (if available).

Exposure to monitoring tools for ML drift, data quality, and performance metrics

Knowledge of data privacy and compliance in AI (e.g., anonymization, fairness, explainability

Educational Qualification:

15 years full time education is required.