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

Experience with real-time inference systems
Exposure to monitoring tools (Prometheus, Grafana)
Knowledge of LLM deployment / RAG pipelines

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)

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.