Key Responsibilities
Design and deploy agentic generative AI solutions leveraging LLMs, RAG pipelines, context engineering, and REST APIs.
Architect and build multi-agent automation systems using frameworks such as LangGraph or similar orchestration platforms
Implement LLM governance architecture including prompt drift detection, auditability, and safety policies to ensure regulatory compliance.
Develop and integrate vector database solutions for semantic search and retrieval-augmented generation.
Collaborate cross-functionally with AI architects, product managers, and compliance teams to accelerate AI adoption across business units.
Develop APIs using FastAPI/Flask and build interactive demos for stakeholder showcases.
Contribute to MLOps practices including CI/CD pipelines, model monitoring, and deployment automation.




Conduct research experiments to enhance model performance, reliability, and fairness.
Build and maintain ML models for domain-specific tasks such as NLP, computer vision, and predictive analytics.

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Required Qualifications

Technical Skills
Programming: Proficiency in Python; working knowledge of SQL and Java.
GenAI & LLMs: Deep expertise in LLMs, RAG, prompt engineering, fine-tuning, and model governance.
Frameworks: LangChain,
APIs & Backend: FastAPI, Flask, REST API design.
Databases: Vector databases (Milvus, PGVector), PostGRESQL.
Cloud & MLOps: Azure AI Foundry; Docker, Kubernetes, CI/CD pipelines.
Visualization & UI: Streamlit, basic UI/UX design principles.

📌 Ai Development Engineer Hyd/ban Hyderabad
🏢 Pragma Edge
📍 Hyderabad