MLOps Engineer

Job Description:

• 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

Key Responsibilities:

• 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

Technical Requirements:

• 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

Additional Responsibilities:

• Experience with real time inference systems
• Exposure to monitoring tools Prometheus Grafana
• Knowledge of LLM deployment RAG pipelines

Preferred Skills:

Analytics->Data Science,Technology->Machine learning->data science,Technology->Machine Learning->Generative AI