Job Description

· Hands-on with LLMs (GPT family, BERT/Transformers), prompt engineering, LLM optimization (context windows, adapters), RAG, and evaluation (golden sets, BLEU/BERTScore/semantic similarity).

Cloud: Azure (incl. Azure AI, OpenAI, Cognitive Services), AWS (Bedrock/Sage Maker), GCP (Vertex AI); containerization (Docker/Kubernetes) and service meshes.

Data & Integration: SQL, NoSQL, data warehousing concepts (fact/dimension, SCD), ETL/ELT, streaming (Kafka/Event Hubs), API design, and secure integration (OAuth2/JWT/RBAC).

LLMOps/MLOps & Automation: CI/CD for models, model registry, feature store, monitoring (tokens, drift, hallucinations), Python automation for infra and pipelines. Engineering Practices

Proficiency in Python, Lang Chain/Lang Graph, vector DBs, and prompt/agent frameworks; strong Git, CI/CD, IaC, and Agile/Scrum delivery. Architecture Leadership

Prior ownership of complex AI solution designs, HLD/LLD, NFRs, and security/ compliance patterns; ability to mentor squads and lead design forums.

Preferred Skills:

Experience designing AI gateways, MCP (Model Context Protocol) implementations, and agent registries; familiarity with metadata standards (DCAT/JSON-LD).

Exposure to enterprise AI platforms/labs and accelerators

Knowledge of infrastructure automation in DC/cloud (orchestration, provisioning) with AI workloads and GPU scheduling (K8s, NCCL, Triton).

Prior work on Microsoft Copilot Studio solutions, conversational agents, and Power Platform extensibility.

Qualifications & Certifications · B.Tech or Equivalent degree · Certifications (any subset): Azure AI Engineer/Architect, Azure OpenAI, AWS/GCP AI, Databricks ML, Kubernetes