Tech Stack:

- Python (Mandatory)

- JavaScript/TypeScript (Preferred)

- Database: Vector DB & Redis

- FastAPI, Django, REST APIs, WebSockets, Async Programming (asyncio), Microservices

Large Language Models (LLMs):

- Experience with OpenAI APIs (Chat Completions / Responses API), GPT-4o / GPT-5 or equivalent, Claude, Gemini (Preferred)

- Understanding of Prompt Engineering, Function Calling/Tool Calling, Structured Outputs, JSON Schema, Context Window Management, Token Optimization, AI Safety, Model Evaluation

Voice AI:

- Hands-on experience with STT, TTS, OpenAI Realtime API, Streaming Audio, Voice Activity Detection (VAD), Realtime WebSocket APIs

Retrieval-Augmented Generation (RAG):

- Experience with Embeddings and Vector Databases (Redis Vector Search, Pinecone, Weaviate, Qdrant, Milvus)

AI Libraries & Frameworks:

- OpenAI SDK, LangChain, LlamaIndex, Hugging Face Transformers, Sentence Transformers, Pydantic AI

Roles & Responsibilities:

- Design and develop AI-powered enterprise applications using LLMs.

- Build Voice AI solutions for real-time conversational systems.

- Develop AI Agents capable of reasoning, planning, and tool execution.

- Design and implement RAG pipelines using vector databases.

- Integrate AI services with REST APIs, WebSockets, SIP, and enterprise systems.

- Develop scalable Python microservices.

- Optimize AI applications for latency, cost, and accuracy.

- Build reusable AI workflows and tool-calling frameworks.

- Collaborate with Product, QA, and DevOps teams.

- Evaluate and integrate emerging AI technologies.