Lead AI Engineer - Python
Description
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.
Skills
- Agentic AI
- AI
- API
- Asyncio
- DevOps
- Django
- Embeddings
- FastAPI
- Hugging Face
- JavaScript
- JSON
- LangChain
- LlamaIndex
- LLM
- Microservices
- Milvus
- Model Evaluation
- OpenAI
- Pinecone
- Prompt Engineering
- Pydantic
- Python
- Qdrant
- RAG
- Redis
- REST
- Sentence Transformers
- Transformers
- TypeScript
- Vector Databases
- Vector Search
- Weaviate
- Websockets