Senior AI/ML Engineer

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.