Senior AI/ML Engineer

Job Description

About the Role

We are looking for a highly driven AI/ML Engineer who has experience operating in early-stage startup environments and can build AI-powered products from concept to production.

This role goes beyond traditional model development — we are seeking someone who can combine machine learning expertise, strong backend engineering, and frontend collaboration skills to ship intelligent, user-facing products rapidly.

The ideal candidate has strong product intuition, has worked in fast-paced startup environments, and is comfortable taking ownership of end-to-end system development.

• Design, build, and deploy AI-powered features and systems.
• Develop and integrate LLM-based applications, AI agents, or multi-agent systems.
• Architect and implement backend services using Python (FastAPI preferred).
• Collaborate on or directly contribute to frontend development using ReactJS where needed.
• Translate product ideas into working AI prototypes quickly.
• Lead technical design discussions and drive system architecture decisions.
• Mentor and guide junior engineers where applicable.
• Work cross-functionally with product and design teams to build user-centric AI solutions.
• Ensure scalability, reliability, and performance of AI systems in production.

• Experience working in an early-stage startup or as part of a founding/core team.
• Strong backend development skills in Python, with hands-on experience in FastAPI or similar frameworks.
• Solid understanding of machine learning fundamentals and practical AI system implementation.
• Experience leading or managing engineering teams.
• Strong product intuition — ability to move from idea → working prototype quickly.
• Excellent communication and collaboration skills.
• Ability to work in ambiguous, fast-moving environments with high ownership.

• Hands-on experience with:
• Large Language Models (LLMs)
• AI Agents
• Multi-agent orchestration frameworks
• Prompt engineering and LLM optimization
• Experience building AI-powered tools or automation systems.
• Familiarity with vector databases, RAG architectures, embeddings, or LLM pipelines.
• Experience deploying ML systems in production environments.
• Exposure to cloud platforms and DevOps practices.