MLOps Engineer

Job Purpose : 'As an MLOps Engineer, you will be responsible for deploying, scaling, and monitoring ML & GenAI models in production. You will own the infrastructure, automation, and observability layer while collaborating with ML Engineers and Data Scientists to ensure models (LLMs, embeddings, recommendation systems, etc.) are reliably served at scale. This role combines DevOps best practices with ML-specific needs, ensuring smooth model lifecycle management and robust GenAI-powered applications.

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..