Al Engineer

Role Overview

You will own the complete AI backend end-to-end - from agent architecture and model integration to deployment, monitoring, and continuous improvement. You are the single owner of the AI 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 AI 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.

Key Responsibilities

• 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.
• 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 pipelines (Python preferred).
• Implement streaming responses and async processing.
• Deploy and manage services using cloud infrastructure (AWS/GCP/Azure).
• Maintain CI/CD pipelines and production environments.
• 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.

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.
• Strong ownership mindset.
• Ability to work across AI, backend, and infrastructure.

Requirements & Skills

• Mandatory:
• Hands-on experience with LLMs and AI systems in production.
• Experience with vector databases and retrieval systems.
• Understanding of distributed systems and async processing.
• Nice to Have:
• Knowledge of prompt engineering at scale.
• Experience with real-time AI applications.