AI Engineering Lead

Location: Hyderabad - On-site

Employment Type: Full-Time

The candidate should be really expert with the below expertise:

• if they have worked realtime in Gen AI , agentic systems, langgraph, Human in loop in agents.
• Did they deploy any gen ai agentic system with langgraph to production stably
• Aware of Evaluation of agents
• And should be more confident in communication as you talk.
• Should be still a bit hands-on in python.
  
  

What We're Looking For

AI Engineering Lead

Required Skills

• 12+ years of professional experience as a software engineer and building applications/systems.
• 2+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques, particularly multi-agent systems.
• Expert proficiency in programming skills in Python, Langgraph, and SQL is a must.
• Expert in architecting GenAI applications/systems using various frameworks & cloud services.
• Expert proficiency in using AI tools like Claude Code, Codex, cursor, windsurf, and the like.
• Expert proficiency in AI observability & evaluation tools like Langsmith, Langfuse, or similar.
• Good proficiency in using various cloud services from Azure, GCP, or AWS for building GenAI applications.
• Experience in driving the engineering team toward a technical roadmap.
• Excellent communication skills to effectively collaborate with business SMEs.
  
  

Roles & Responsibilities

Solutioning & Lead

• Build the technical roadmap given a business requirement and own the delivery of the same.
• Lead the engineering team toward a technical roadmap and ensure the timely execution of the roadmap to achieve customer satisfaction.
• Design robust multi-agent architectures, including supervisor-router patterns with dynamic sub-agent routing and stopping conditions.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
  
  

Hands-on skills

• Develop LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like retrieval-augmented generation (RAG) and multi-agent-based architectures.
• Build and maintain agent evaluation pipelines, including offline eval datasets, LLM-as-judge, and CI-integrated eval runs.
• Codebase ownership: Build & maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
• Cloud integration: Deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.