AI Engineer (MLOps / LLM)

Key Responsibilities:

• Build and deploy end-to-end ML & LLM pipelines
• Develop RAG systems and multi-agent AI workflows
• Manage model lifecycle (experiment → registry → production)
• Containerize and deploy using Docker & Kubernetes
• Implement CI/CD pipelines for ML/AI systems
• Monitor model performance, drift, and reliability in production
• Collaborate with data scientists to productionize models
• Integrate AI solutions into scalable enterprise platforms

Core Tech Skills:

• Strong Python (production-grade, scalable code)
• MLOps: MLflow / Weights & Biases, model registry & lifecycle
• LLMs & GenAI: OpenAI / open-source models, embeddings, RAG, agentic workflows
• AI Frameworks: LangChain / LlamaIndex (or similar orchestration tools)
• Data Pipelines: Feature engineering, preprocessing, model evaluation
• Containers & Orchestration: Docker, Kubernetes (K8s)
• CI/CD: GitLab / Jenkins for ML pipelines
• Cloud & Compute: GPU workloads, cloud ML infrastructure (AWS/Azure/GCP)
• Monitoring: Model performance, drift detection, logging