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, effective 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.

📌 AI Engineering Lead (Hyderabad)
🏢 ALOIS UK
📍 Hyderabad