Work Location: Hyderabad (Hybrid Model)

Prestige Skytech, Financial District

Experience: 5 - 8 years

Mandatory Skills: AI, ML, RAG, LLM

Key Responsibilities
Agentic AI Development:
Build, customize, and deploy AI agents using frameworks like LangChain, AutoGen, CrewAI, and Haystack.
Enable agent reasoning, planning, and tool-use for complex tasks.
RAG Pipeline Design:
Implement and optimize RAG pipelines for enterprise-scale knowledge retrieval.
Work with vector databases (Pinecone, FAISS, Weaviate, Milvus) to manage embeddings and context injection.
Fine-tune retrieval strategies, chunking logic, and metadata tagging for high-quality responses.
Prompt Engineering & LLM Integration:
Develop structured prompts, context-aware query chains, and workflows for LLMs (OpenAI, Anthropic, Llama, Mistral, etc.).
Integrate RAG-enabled LLMs into APIs, chatbots, and enterprise applications.
Automation & Platform Development:
Create orchestration pipelines for AI agents and RAG workflows.
Contribute to building internal AI platforms, dashboards,



and monitoring systems.
Experimentation & Research:
Stay current with recent developments in RAG, multi-agent systems, and reasoning models.
Rapidly prototype AI solutions to demonstrate value to business teams.

Required Skills
Programming: Robust in Python; familiarity with JavaScript/TypeScript or Go is a plus.
LLM Frameworks: Experience or coursework in LangChain, LlamaIndex, Haystack, or AutoGen.
RAG Expertise: Understanding of RAG concepts, document indexing, embeddings, retrieval strategies, and vector DBs.
Databases: PostgreSQL/MySQL for structured data; Pinecone, Weaviate, Milvus, FAISS for vectors.
APIs & Cloud: Knowledge of REST/GraphQL APIs and cloud services (AWS/GCP/Azure).
Version Control: Git, GitHub/GitLab, and CI/CD pipelines.

Preferred Skills (Valuable-to-Have)
Familiarity with LangGraph and other agent orchestration libraries.
Exposure to multi-agent collaboration patterns

📌 Ai Engineer Hyderabad
🏢 Blumetra
📍 Hyderabad