Lead AI Engineer Pandora R&D Lab

Appsierra Group; Hyderabad; On-site; INR 1,500,000 - 2,400,000; Full time

Job summary

Appsierra Group in Hyderabad is seeking a Lead AI Engineer to design and build AI-powered enterprise apps using large language models. You will lead development of voice AI, RAG pipelines, and tool-calling frameworks, and you will scale Python microservices for real-time performance.

Ideal candidates have 5-8 years of experience, strong Python and OpenAI API expertise, experience with LangChain and vector databases, and hands-on work with streaming, VAD, and structured outputs.

Qualifications

• 5–8 years of experience in AI/ML engineering.
• Strong Python and OpenAI API experience.
• Hands-on with LangChain, vector databases, and RAG pipelines.

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.

Skills

Python

LLMs & Prompting

Voice AI

RAG pipelines

REST/WebSocket

Education

Any Graduation

Tools

OpenAI SDK

LangChain

LlamaIndex

Hugging Face Transformers

Sentence Transformers

Pydantic AI

Job description

Job Title / Role Lead AI Engineer

Type of Employment (Full

time/Contract/Contract to hire) Ful time

Years of Experience Required (Min-Max) 5 - 8 years

No. of Positions 2

CTC in INR 15- 24 LPA

Bounty

Highest Qualification Required Any Graduation

Work Location Hyderebad

Mode of Work (Remote/On-site/Hybrid) 5 Days WFO, [ Sunday & Monday weekoff]

Notice Period/ Start Date Flexibility 0 - 15 Days

On-site feasibility (work abroad) N/A

Interview Rounds

L1 - Technical Coding Assesmet

L2 - Technical [F2F]

L3 -techno Managrial

HR

Interview Panel Time slots 11 AM to 6 PM

Office Time 10 AM to 7 PM

Weekend Drives Yes

Salaries paid on Last working day of the month

Must/Good to Have Details

Must have

Only Male candidates are preffred for this role

Programming Languages

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

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
• 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 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, 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.