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 tradeoffs that directly impact the business
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 current domain-specific agents with tailored retrieval, prompts, tools, and fallback strategies Develop graph-based routing logic for energetic task handling
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
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
Backend & Infrastructure Develop scalable backend services for AI pipelinesImplement streaming responses and async processing Deploy and manage services using cloud infrastructure Maintain CI/CD pipelines and production settings
Observability & Reliability Build monitoring systems for latency, errors, and model performance Track hallucinations, failure cases, and edge scenarios Implement fallback systems and reliability layers
Cost & Performance Optimization Optimize inference costs across models and workflows Balance cost vs quality vs latency in production systems Continuously improve system efficiency.

Pay: ₹500,000.00 - ₹600,000.00 per year

Work Location: In person

📌 Al Engineer Full Stack Al Backend & Infrastructure Pune
🏢 Verdeshell Technologies Private
📍 Pune