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.
Key Responsibilities
- 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.
Required Qualifications
- 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.
Preferred / Bonus Qualifications
- 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.