AI / ML Engineer

You will sit on the Engineering team and own the design and development of Unosecur's Generative AI and agentic capabilities, including LLM-backed services, agent orchestration, and the architecture connecting AI systems to the security platform. You'll work closely with the founding and senior engineering team and partner with backend, cloud, and product teams.

The candidate will have responsibilities across the following functions:

GenAI and Agentic Systems:

• Design and architect end-to-end GenAI and agentic systems, from problem framing through orchestration, retrieval, and production deployment.
• Build agentic workflows using LangChain, LangGraph, and MCP.
• Integrate tools, plugins, and external data sources with clear permission and failure boundaries.

RAG and Retrieval:

• Build and continuously improve RAG pipelines.
• Evaluate retrieval quality and improve retrieval performance.
• Work with vector and embedding databases.

Model Strategy:

• Own model-selection strategy across LLMs and SLMs.
• Balance model quality, latency, cost, security, and data-handling requirements.

Engineering and Reliability:

• Build reliable and scalable AI services capable of running at production load.
• Improve observability, quality, and reliability across AI systems.
• Partner with Product and Security teams to translate ambiguous problems into safe, well-architected AI capabilities.

Technical Leadership:

• Mentor engineers across GenAI initiatives.
• Act as the technical reference point for Generative AI within the engineering team.

Requirements:

• 10+ years of overall engineering experience, including 3+ years of hands-on experience with Generative AI / LLM-based solutions.
• Demonstrated experience designing and architecting AI solutions, not just implementing or consuming AI tools.
• Ability to demonstrate AI systems personally designed and built.
• Strong expertise in the GenAI/agentic stack, including: LangChain, LangGraph, MCP, RAG, vector databases, Multiple LLMs / SLMs.
• Strong engineering fundamentals, including: Data structures, problem-solving, scalable distributed services
• Working knowledge of GenAI platforms such as AWS Bedrock, Google Vertex AI, or similar.
• Experience with AWS or GCP and cloud security fundamentals.
• Strong ownership and builder mindset.
• Comfortable operating in startup environments with ambiguity.
• Strong judgment when making architecture and technical trade-offs.

Nice to Have:

• Open-source AI contributions or substantial hands-on experience with open-source AI projects.
• Experience building AI into security, identity, or compliance-sensitive products.
• Experience with RAG evaluation tooling such as Ragas.
• Structured approaches to measuring and improving AI quality.
• Product-based or startup experience with end-to-end system ownership.

Skills

AWS, GCP, Gen AI, Generative AI, Google Cloud, LLM, LLMs, LangChain, LangGraph, MCP, RAG