Must have:
Programming Languages
• Python (Mandatory)
• JavaScript/TypeScript (Preferred)
• Database [Vector DB & Redis]
Backend Development
• Fast API
• Django
• REST APIs
• WebSocket's
• Async Programming (Asencio)
• 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
• 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
• We aviate
• Qanat
• Milvus
AI Libraries & Frameworks
Strong experience with:
• OpenAI SDK
• Lang Chain
• Llama Index
• Hugging Face Transformers
• Sentence Transformers
• Pedantic AI
Preferred Domain Experience
• Conversational AI
• Voice AI
• AI Assistants
• Customer Support Automation
• Workflow Automation
Good to have
• MCP (Model Context Protocol)
• AI Guardrails
• Prompt Versioning
• Cost Optimization
• AI Evaluation Frameworks
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, WebSocket's, 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.