AI Engineering Tech Lead / Architect

Faros AISan Mateo, CA
33d$210,000 - $250,000Onsite

About The Position

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.

Requirements

  • 5+ years of professional experience in software engineering with a focus on Machine Learning or AI.
  • Strong proficiency in Python
  • Strong familiarity with libraries such as NumPy, Pandas, and Scikit-learn.
  • Prompt engineering and evaluation frameworks.
  • LLM/GenAI: experience hardening and productionalizing applications based on GenAI.
  • Experience designing/consuming REST/GraphQL APIs and deploying applications using containerization ecosystems (Docker, Kubernetes).
  • English C1: able to participate in technical calls and write clear communication.
  • Degree in Computer Science, Mathematics, related field, or equivalent real-world experience.

Nice To Haves

  • Experience with building & integrating with SaaS platforms.
  • Experience with engineering analytics or developer-productivity tooling.
  • Experience with development pipelines for ML and AI (e.g., MLflow, Weights & Biases, Evaluations, etc).
  • Hands-on experience using coding agents (AI-assisted development).

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.
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