As an AI Engineer, you will be responsible for building full-stack generative and agentic AI solutions - spanning from prompt logic and orchestration to APIs, UIs, and infrastructure integration. You will work in a cross-functional delivery pod to implement scalable, reusable components and rapidly transition PoCs into production-ready systems.

This role requires hands-on fluency across GenAI tools, LLM frameworks, and integration patterns, with the ability to balance experimentation with disciplined engineering practices.

Know your team (Legacy Rewired, Engineering the Future)

At ValueMomentums Engineering Centre, we are a team of passionate engineers who thrive on tackling complex business challenges with innovative solutions while transforming the P&C insurance value chain. We achieve this through a strong engineering foundation and by continuously refining our processes, methodologies, tools, agile delivery teams, and core engineering archetypes. Our core expertise lies in six key areas : Cloud Engineering, Application Engineering, Data Engineering, Core Engineering, Quality Engineering, and Domain expertise.

Key Responsibilities :

- Design, implement, and maintain generative AI and agentic systems end-to-end.

- Develop APIs, prompt orchestration logic, and integrations for LLM-driven applications.

- Build scalable and reusable AI components for production deployment.

- Work with cross-functional teams to implement rapid PoC-to-production transitions.

- Apply best practices for observability, logging, prompt tracing, metrics collection, and evaluation scaffolding.

- Collaborate on CI/CD pipelines and containerized deployments for AI applications.

- Optionally, integrate AI solutions with web UIs (React, Next.js) or domain-specific workflows in insurance.

Required Skills & Qualifications :

- 3- 6 years of hands-on software engineering experience.

- Strong programming skills in Python.

- Experience with LLM frameworks such as LangChain, LlamaIndex, or similar.

- Hands-on experience with APIs, containerized applications, and CI/CD practices.

- Understanding of generative AI patterns, including RAG (Retrieval-Augmented Generation), agentic loops, embeddings, streaming, and context injection.

- Familiarity with observability tools, prompt evaluation frameworks, and system metrics.