AI Engineering Architect

Mission 🚀

At Faros, we are building the engineering intelligence platform for the AI era. We help engineering organizations understand the impact of AI on software delivery while providing the operational context that enables AI agents to operate with greater accuracy, relevance, and trust in complex enterprise environments. Our platform is used by some of the most sophisticated engineering teams in the world, ranging from fast-growing technology companies to F500 enterprises. We believe the future of software development will be built by humans and AI working together, and we're building the foundation that makes that possible.

About the Role

Join our AI team to build Lighthouse AI - the intelligence engine that helps engineering leaders understand organizational bottlenecks and improve performance in an age where AI is redefining the engineering craft. You'll work on natural language interfaces, causal analysis models, and ML-driven insights that impact how thousands of engineers work at companies like Autodesk, Discord, and Vimeo.

Beyond generating insights, you will help pioneer how AI agents operate safely and effectively inside complex enterprise engineering environments. This includes designing the operational context, evaluation frameworks, and feedback loops that allow agents to reason over real engineering workflows, measure the quality and impact of their actions, and continuously improve over time. You will work at the intersection of data infrastructure, AI systems, and engineering operations, helping define how humans and AI collaborate to build software together.

Responsibilities

• Collaborate with product, data, and engineering teams to define, design, and integrate AI features seamlessly into the user experience.

• Build and maintain scalable ML pipelines and infrastructure balancing speed, flexibility, security, and scalability.

• Build and optimize LLM-powered features including natural language query generation, data summarization, and data exploration tools.

• Design and implement prompt engineering strategies, evaluation frameworks, and iterate on model performance.

• Fine-tune Large Language Models (LLMs) or custom architectures for performance, latency, and cost-efficiency.

• Stay up-to-date with the latest research (e.g., papers from NeurIPS, ICML) and identify opportunities to apply state-of-the-art techniques to business problems.

What We Value

• We favor transparency and honest debate because strong decisions depend on saying the hard things out loud.