Data Scientist

Key / Primary Responsibilities: '

'* Design and manage CI/CD pipelines for ML model deployment.

* Provision and maintain scalable infrastructure using Kubernetes, Docker, AWS Lambda, or cloud services (AWS/GCP/Azure).

* Automate model deployment workflows, ensuring reproducibility and version control.

* Implement monitoring, alerting, and observability for ML systems (latency, throughput, data drift, failures).

* Integrate with model orchestration pipelines (Airflow, Prefect) to support retraining and inference workflows.

* Manage model registry, versioning, and rollout strategies (blue/green, canary deployments).

* Ensure security, compliance, and cost optimization for ML infra.

* Manage vector databases (Opensearch, Pinecone, FAISS, Milvus) for GenAI retrieval pipelines.

Secondary Responsibilities:

* Work closely with ML Engineers and Data Scientists to understand model requirements.

* Support real-time and batch inference pipelines by ensuring infra scalability and resilience.

* Troubleshoot deployment and runtime issues across containers, APIs, and cloud services.

* Document and standardize infrastructure-as-code practices (Terraform, Helm, etc.).

* Manage GPU/accelerator infra for LLM fine-tuning or inference optimization.

Key Success Metrics: Model Uptime and Reliability, Model Performance in production, Efficiency of Spark Jobs, Workflow Automation Efficiency, User Satisfaction and Internal Feedback.

Professional Degree: Any B.E/B.Tech

Mandatory Certification: Machine Learning or Data Science.

Managerial & Leadership Responsibilities: 'Leading a team of 4 Data scientist and ML engineers to deliver on primary and secondary responsiblities..