Principal AI Engineer

Overview

Develop and deploy enterprise-grade generative AI solutions for the AI Enablement Team, using LLM platforms, cloud AI services and API integration frameworks.

What you'll do

• Design, build, and deploy autonomous multi-agent workflows using orchestration frameworks such as LangGraph, CrewAI, Autogen, or similar, including complex state machines with conditional routing, parallel execution, and error recovery patterns.
• Architect graph-based agent workflows with 10+ nodes involving agent collaboration, task decomposition, and sequential/parallel execution across multiple business domains.
• Develop and maintain reusable agent node libraries, extensible platform patterns, versioning strategies, and testing frameworks (unit, integration, and end-to-end) for agent workflows.
• Build production-grade FastAPI applications with async I/O patterns, integrating PostgreSQL, Redis, and external enterprise services.
• Implement real-time agent streaming using Server-Sent Events (SSE) and WebSocket protocols, alongside RESTful and event-driven API architectures for agent orchestration.
• Integrate cloud-based LLM providers (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4) and design prompt management systems with versioning, templating, and dynamic compilation.
• Implement conversation state persistence using Redis checkpointing and build tool-calling protocols (Model Context Protocol, function calling) for external data sources and APIs.
• Develop hybrid intelligence patterns combining LLM reasoning with rule-based logic and statistical analysis, and build response transformation pipelines for structured analytical outputs.
• Integrate observability platforms (Langfuse, LangSmith, or similar) to enable end-to-end agent tracing, telemetry, performance monitoring, and cost optimization across production workflows.
• Implement evaluation frameworks measuring agent success rates, reasoning quality, and output accuracy, while continuously optimizing token usage and LLM costs.
• Ensure enterprise security integration (LDAP, SSO, access control), robust error handling, and compliance with data governance and Responsible AI standards.
• Partner with data engineers, business analysts, and UX teams to translate requirements into scalable agent workflows and streaming interfaces.
• Mentor junior engineers on async Python patterns, agent design, and LLMOps best practices; participate in architecture reviews and contribute to documentation and knowledge sharing.

What you'll need

• Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or a related discipline.
• 9+ years of software engineering experience.
• 2+ years building and deploying production LLM-powered applications.
• Proven experience with agentic orchestration frameworks: LangGraph (preferred), LangChain, CrewAI, Autogen, or similar.
• Proven experience with cloud LLM providers: AWS Bedrock, Azure OpenAI, Anthropic Claude, or OpenAI GPT-4.
• Proven experience with production async web frameworks, particularly FastAPI.
• Proven experience with Docker containerization, Git version control, and CI/CD pipelines.
• Proven experience with cloud platforms: AWS, Azure, or Google Cloud.
• Expert-level async Python programming (asyncio, async/await patterns).
• Demonstrated experience building autonomous agent systems performing multi-step tasks beyond simple chatbots, including conditional logic, state management, and tool-calling patterns.
• Strong understanding of agentic design patterns including ReAct, Plan-and-Execute, and tool-use agents.
• Experience implementing Model Context Protocol (MCP) and agent-to-agent (A2A) communication frameworks.
• Familiarity with LLMOps practices, observability tooling (Langfuse, LangSmith), and prompt engineering at scale (templating, versioning, optimization).
• Excellent analytical, problem-solving, and communication skills with the ability to work effectively in globally distributed teams.

Nice to have

• Prior experience in global life sciences, especially in the GPS functional area.
• Experience managing or collaborating with offshore technical development teams and diverse international stakeholders.

Details

• Location: Hyderabad, Telangana, India.
• Work model is assigned based on core responsibilities (site-essential, site-by-design, field-based or remote-by-design).