Role Overview

You will own the complete Al backend end-to-end - from agent architecture and model

integration to deployment, monitoring, and continuous improvement. You are the single

owner of the Al stack: designing agents, building retrieval pipelines, optimizing costs,

deploying to production, and iterating based on real-world usage.

This role sits at the intersection of Al engineering, backend engineering, and DevOps. You

won't just write prompts — you will architect systems, ship production code, debug

production failures, and make cost-vs-quality trade offs that directly impact the business.

Responsibilities

1. Multi-Agent System Architecture

- Design and scale a multi-agent orchestration system with 10+ specialized agents
- Implement parallel execution (fan-out/fan-in), conditional routing, and shared state handling
- Build new domain-specific agents with tailored retrieval, prompts, tools, and fallback strategies
- Develop graph-based routing logic for dynamic task handling

2. LLM Integration & Optimization

Manage multi-model, multi-provider architecture

- Select models based on cost, latency, and accuracy tradeoffs
- Build and refine prompt systems (few-shot, structured reasoning, domain tuning)
- Optimize token usage, latency, and response quality

3. Retrieval & Knowledge Systems

- Build hybrid search pipelines (vector + keyword + metadata filtering)
- Optimize chunking, indexing, embeddings, and re-ranking strategies
- Ensure high-quality context retrieval for LLM pipelines

4. Backend & Infrastructure

- Develop scalable backend services for AI pipelines




- Implement streaming responses and async processing
- Deploy and manage services using cloud infrastructure
- Maintain CI/CD pipelines and production environments

5. Observability & Reliability

- Build monitoring systems for latency, errors, and model performance
- Track hallucinations, failure cases, and edge scenarios
- Implement fallback systems and reliability layers

6. Cost & Performance Optimization

- Optimize inference costs across models and workflows
- Balance cost vs quality vs latency in production systems
- Continuously improve system efficiency.

Required Skills

- Strong experience with backend development (Python preferred)
- Hands-on experience with LLMs and AI systems in production
- Experience with vector databases and retrieval systems
- Understanding of distributed systems and async processing
- Familiarity with cloud platforms (AWS/GCP/Azure)
- 4+ Years of experience

Nice to Have

- Experience with multi-agent frameworks
- Knowledge of prompt engineering at scale
- Experience with real-time AI applications
- Exposure to monitoring and observability tools
- Robust ownership mindset
- Ability to work across AI, backend, and infrastructure
- Focus on shipping production-ready systems
- Practical problem-solving over theoretical approaches.

Pay: ₹500,000.00 - ₹1,631,624.32 per year

Benefits:

- Flexible schedule
- Food provided
- Paid sick time
- Provident Fund

Application Question(s):

- What is your current CTC?
- How many years of experience you have as an AI Engineer?

Work Location: In person

📌 Al Engineer - Full Stack Al Backend & Infrastructure (Kharadi)
🏢 HRnest
📍 Kharadi