Job Description :

What You'll Do :

- Deploy, scale, and operate ML and Generative AI systems in cloud-based production environments (Azure preferred).

- Build and manage enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines.

- Implement and operationalise agentic AI workflows with tool use, leveraging frameworks such as Lang Chain and Lang Graph.

- Develop reusable infrastructure and orchestration for GenAI systems using Model Context Protocol (MCP) and AI Development Kit (ADK).

- Design and implement model and agent serving architectures, including APIs, batch inference, and real-time workflows.

- Establish best practices for observability, monitoring, evaluation, and governance of GenAI pipelines in production.

- Integrate AI solutions into business workflows in collaboration with data engineering, application teams, and stakeholders.

- Drive adoption of MLOps / LLM Ops practices, including CI/CD automation, versioning, testing, and lifecycle management.

- Ensure security, compliance, reliability, and cost optimisation of AI services deployed at scale.

What You Know :

- 5 - 9 years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment roles.

- Strong proficiency in Python, with experience building production-ready AI/ML services and workflows.

- Proven experience deploying and supporting GenAI applications in real-world enterprise environments.

- Experience with orchestration frameworks, including but not limited to Lang Chain, Lang Graph, and Lang Smith.

- Strong knowledge of model serving inference pipelines, monitoring, and observability for AI systems.

- Experience working with cloud AI ecosystems (Azure AI, Azure ML, Databricks preferred).

- Familiarity with containerization and deployment tools (Docker, Kubernetes, REST) and Role & responsibilities.