Lead AI Product Engineer – Python & LLM Agents

Overview:

We are looking for an AI Product Engineer to join our team full-time on-site. This role is for someone with good LLM understanding, strong Python development skills, and hands-on experience in prompt and context engineering for building agents. You will own the technical roadmap of our AI Agents, ensuring smooth delivery from concept to deployment, and translating research breakthroughs into usable product features.

Key Responsibilities:

• Build scalable agentic AI systems

  leveraging modern LLMs such as Gemini, ChatGPT, and others.

• Design, prototype, and optimise LLM-driven workflows

  using advanced prompt engineering, context management, and reasoning frameworks.

• Own the AI product roadmap

  end-to-end, ensuring timely, high-quality execution of features and improvements.

• Develop clean, efficient Python code

  for backend services, API integrations, and AI/ML pipelines.

• Use AI-assisted coding tools

  (e.g.,

  Claude

  ,

  Cursor

  ,

  GitHub Copilot

  ) to accelerate development workflows.

• Debug, optimise, and enhance AI features

  in collaboration with engineering, product, and design teams.

• Work cross-functionally

  to translate user needs into reliable AI features and product enhancements.

Minimum Requirements:

• 2+ years of professional software development experience.
• Strong understanding and practical usage of LLMs, including

  prompt design, context engineering, and agent frameworks

  .
• Hands-on experience building

  AI agents or agent-like systems,

  capable of autonomous or semi-autonomous decision-making, tool/API usage, and end-to-end task execution.
• Strong Python skills, especially for backend logic, automation, and AI API integrations.
• Experience integrating and deploying AI APIs (OpenAI, Anthropic, Google, etc.).
• Ability to

  independently drive projects

  from concept to launch with minimal supervision.
• B.Tech / B.E. in any discipline.
• Solid understanding of

  fundamental software architecture

  and system scaling considerations.

Nice-to-Have Skills:

• Experience working with cloud platforms such as

  AWS, GCP, or Azure

  .
• Familiarity with vector databases, embeddings, or retrieval-augmented architectures.
• Knowledge of DevOps or MLOps workflows.