What We're Looking For :

- 6 to 10 years of software engineering experience, including senior/lead ownership of production systems end to end.

- Deep fundamentals - system design, API design, testing, CI/CD, debugging production issues at scale. You need these to build agents that operate on real codebases.

- Strong hands-on AI engineering : LLMs, prompt engineering, RAG, agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen), vector databases and embedding pipelines.

- Track record of shipping AI systems to production - not experiments.

- Python depth and real working knowledge of at least one full stack (MERN/MEAN or Java/Spring Boot).

- Experience leading a small engineering team.

- A clear, defensible point of view on where AI genuinely accelerates the SDLC and where it doesn't - you'll be asked to justify it by client architects who are sceptical by default.

Key Responsibilities :

- Architect and deploy end-to-end Agentic AI solutions that automate complex business processes, ensuring high performance and reliability for client operations.

- Lead the integration of Large Language Models (LLMs) and Vector Databases into existing enterprise ecosystems to enhance search, retrieval, and generative capabilities.

- Oversee the full software development lifecycle, utilizing Java, Spring, and full-stack frameworks to build scalable, secure, and maintainable applications.

- Mentor junior engineering talent and foster a culture of technical excellence, ensuring code quality and architectural integrity across all project phases.

- Collaborate directly with client stakeholders to translate ambiguous business challenges into structured technical roadmaps and actionable AI strategies.

- Optimize system performance and latency by refining data pipelines and model inference strategies to meet rigorous production standards.