AI Engineer

• Pre-Series A startup with $5M in funding. We're a fast-growing B2B security SaaS platform
• making identity security smarter and simpler for enterprises worldwide. You’ll be part of a
• diverse team that thrives on creativity, collaboration, and cross-border problem-solving. With
• cybersecurity now mission-critical, you’ll be building not just a career, but a future in one of
• tech’s most dynamic and resilient sectors.
• What your experience will be
• You will sit on the engineering team and own the design and build of our Generative AI and
• agentic capabilities — the LLM-backed services, agent orchestration, and the architecture that
• connects them to our security platform. You’ll work closely with the founding and senior
• engineering team and report into engineering leadership, partnering day to day with backend,
• cloud, and product. Your toolkit spans LLMs and small language models, agent frameworks
• such as LangChain and LangGraph, MCP, RAG pipelines and vector databases, and GenAI
• platforms like AWS Bedrock and Google Vertex AI, deployed on cloud infrastructure (AWS /
• GCP). This is a Bengaluru-based role.
• Why you belong here and how you will grow
• This is a small, senior team where the person who designs a system is the person who ships it
• — you won’t be handed someone else’s blueprint. You’ll set the technical direction for AI in the
• product, mentor engineers around you, and build credibility with founders who are close to the
• work. Because we are early, the surface area is wide: you’ll grow as an architect, as a hands-on
• builder at the edge of the GenAI field, and as a technical leader whose decisions visibly move
• the company.
• What success looks like

• 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, integrating tools, plugins,

and external data sources with clear permission and failure boundaries.

• Stand up and continuously improve RAG pipelines, including retrieval quality evaluation

and the vector / embedding database layer underneath them.

• Own model-selection strategy across LLMs and SLMs, balancing quality, latency, cost, and

the security and data-handling constraints of our domain.

• Raise the engineering bar — reliability, observability, and quality — for AI services running

at production load.

• Partner with product and security to translate ambiguous problems into well-architected,

safe AI capabilities.

• Mentor engineers and act as the technical reference point for GenAI across the team.
• 10+ years of overall engineering experience, including 3+ years hands-on in Generative AI / LLM-based solutions.
• Demonstrated experience designing and architecting AI solutions — not just implementing or consuming AI tools. You can point to systems you personally built.
• Depth in the agentic / GenAI stack: agent orchestration (LangChain / LangGraph), MCP, RAG, vector databases, and multiple LLMs / SLMs with a real point of view on model selection.
• Strong engineering fundamentals — data structures, problem-solving, and the ability to build scalable, reliable distributed services.
• Working knowledge of a GenAI platform (AWS Bedrock, Google Vertex AI, or similar) and cloud (AWS / GCP), plus cloud security fundamentals.
• Behavioral: a builder’s bias for ownership, comfort operating with startup ambiguity, and the judgment to make sound architecture trade-offs.

Nice to have

• Open-source AI contributions or substantial open-source hands-on use.
• Experience building AI into a security, identity, or other compliance-sensitive product.
• RAG evaluation tooling (e.g., Ragas) and structured approaches to measuring AI quality.
• Product-based or startup background where you owned systems end to end.