Lead AI Engineer

"Only Male candidates are preferred for this role"

Opportunity Description Lead AI Engineer

Our end client a research and technology-driven organization focused on Artificial Intelligence, Machine Learning, computer vision, and advanced software solutions develops innovative, AI-powered technologies and intelligent systems for real-world applications. The organization combines research expertise with strong engineering capabilities to deliver scalable, next-generation AI solutions.

They are looking for an experienced Lead AI Engineer to drive the design, development, and deployment of advanced AI solutions. This is a high-impact opportunity for professionals who enjoy solving complex AI challenges, leading technical initiatives, and building innovative, production-ready intelligent systems.

Role & 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.

Required candidate skills

Programming Languages

• Python (Mandatory)
• JavaScript/TypeScript (Preferred)
• Database [Vector DB]

Backend Development

• FastAPI
• Django
• REST APIs
• WebSockets
• Async Programming (asyncio)
• Microservices
• Redis

1. Large Language Models (LLMs)

Must have experience with:

• OpenAI APIs (Chat Completions / Responses API)
• GPT-4o / GPT-5 or equivalent LLMs
• Claude
• Gemini (Preferred)

Good understanding of:

• Prompt Engineering
• Function Calling / Tool Calling
• Structured Outputs
• JSON Schema
• Context Window Management
• Token Optimization
• AI Safety
• Model Evaluation

2. Voice AI

Hands-on experience with:

• Speech-to-Text (STT)
• Text-to-Speech (TTS)
• OpenAI Realtime API (Preferred)
• Streaming Audio
• Voice Activity Detection (VAD)
• Realtime WebSocket APIs

3. Retrieval-Augmented Generation (RAG)

Experience with:

• Embeddings
• Vector Databases

Preferred databases:

• Redis Vector Search
• Pinecone
• Weaviate
• Qdrant
• Milvus

AI Libraries & Frameworks

Strong experience with:

• OpenAI SDK
• LangChain
• LlamaIndex
• Hugging Face Transformers
• Sentence Transformers
• Pydantic AI (Preferred)

Preferred Domain Experience

• Conversational AI
• Voice AI
• AI Assistants
• Customer Support Automation
• Workflow Automation

Nice to Have

• MCP (Model Context Protocol)
• AI Guardrails
• Prompt Versioning
• Cost Optimization
• AI Evaluation Frameworks