- Design and build end-to-end AI systems that power real-world, production-grade use cases across industries like healthcare, insurance, and commerce.
- Prototype and experiment - rapidly turn new ideas into functional proof-of-concepts that push the boundaries of human-centric automation.
- Evaluate and fine-tune models, from classical ML pipelines to LLM-based reasoning systems, optimizing for performance, explainability, and reliability.
- Develop rigorous evaluation frameworks to ensure model behavior, alignment, and performance meet production standards.
- Deploy monitoring and observability tools for AI systems - tracking anomalies, staleness, and drift to maintain model health over time.
- Design agentic workflows and dynamic context engines that enable adaptive, intent-aware automation for business processes.
- Lead and mentor a small team of engineers through the full lifecycle - from architecture and experimentation to deployment, monitoring, and iteration
Lead AI Engineer
Brief Description
Preferred Skills
- 5+ years of professional experience in applied machine learning or AI systems development, with at least 2 years leading projects or small teams.
- Proven expertise in end-to-end model development, deployment, and iteration from research to production.
- Strong foundation in both deep learning and classical ML frameworks, with the ability to choose the right approach for the problem.
- Hands-on experience building LLM-powered applications, including fine-tuning, evaluation, and context engineering.
- Strong grasp of model evaluation, drift detection, and observability in production environments.
- Creative problem-solving mindset - comfortable generating or curating synthetic or weakly supervised data when needed.
- Experience designing or implementing agentic systems, reasoning pipelines, or AI orchestration layers is a strong plus.
- Ability to mentor engineers, collaborate across disciplines, and translate abstract AI ideas into reliable, human-aligned systems.