We are looking for a hands-on MLOps Engineer who will work closely with Data Scientists and ML Engineers to build, deploy, monitor, and optimize machine learning models in production.

This role is NOT focused on platform engineering or infrastructure-only work—instead, it emphasizes end-to-end ML lifecycle management, model deployment, and operationalization of ML systems.

Strong Python programming

Hands-on experience in Machine Learning workflows

Experience with MLOps tools:

MLflow / Kubeflow / Airflow / SageMaker / Vertex AI

Knowledge of CI/CD tools (GitHub Actions, Jenkins, etc.)

Experience deploying models using:

Docker, REST APIs, Flask/FastAPI

Understanding of:

Model versioning

Experiment tracking

Model monitoring

Exposure to cloud platforms (AWS / Azure / GCP) for ML deployment

Knowledge of LLMOps / GenAI deployment pipelines

Familiarity with:

Feature stores

Data pipelines (Spark, Kafka)

Experience with Kubernetes (basic deployment level)

Experience with real-time inference systems

Exposure to monitoring tools (Prometheus, Grafana)

Knowledge of LLM deployment / RAG pipelines