Key Responsibilities
RAG Architecture: Design and optimize Retrieval-Augmented Generation pipelines, ensuring high-quality context retrieval through advanced embedding techniques and vector databases.
Rapid Prototyping: Drive the innovation cycle by building high-fidelity Proof of Concepts (PoCs) for new AI-powered features.
API & Backend Systems: Develop high-performance APIs and microservices (using Python/FastAPI or Node.js) to manage AI inference workflows and data processing.
LLM Orchestration: Integrate and fine-tune interactions with various LLM providers (OpenAI, Anthropic, or Open Source via HuggingFace) using orchestration frameworks.

Technical Requirements
AI & LLM Specialization:
Frameworks: Proficiency in LangChain, LlamaIndex, or Haystack.
Vector Databases: Hands-on experience with Pinecone, Milvus, Weaviate, or ChromaDB.
Models: Deep understanding of prompting, context window management, and model selection across OpenAI, Claude, and Llama 3.
Concepts:



Solid grasp of semantic search, tokenization, and embedding models.

Full-Stack Engineering
Backend: 3+ years of experience in Python (FastAPI/Flask) or Node.js. Knowledge of.NET is a solid plus.
Frontend: Expertise in React.js or Next.js for building responsive, AI-driven user interfaces.
Cloud & DevOps: Familiarity with Docker and deployment on cloud platforms (AWS, Azure, or GCP).
Databases: Experience with both SQL (PostgreSQL) and NoSQL environments.

Preferred Qualifications
Proven track record of building and deploying AI side projects or commercial PoCs.
Experience with Agentic Workflows (e.g., CrewAI or AutoGen).
Robust understanding of AI evaluation metrics and monitoring.

Advantages:
Health insurance
Provident Fund

Work Location: In person

📌 Dotnet With Ai/ml Expertise Hyderabad
🏢 Revalsys Technologies
📍 Hyderabad