AI Engineering Lead
About The Position
LFL is closely collaborating with illumend, an AI-first partnership acceleration platform that’s building solutions in the insurance sector. The partnership leverages 15 years of insurance compliance expertise with LFL's creative technology and applied AI capabilities. The illumend platform aims to make meaningful risk management accessible to all businesses by providing an intuitive interface for non-insurance professionals to track compliance. We are seeking a strategic and technical AI Engineering Lead to spearhead the development, deployment, and optimization of our Machine Learning models and Generative AI workflows. This is a high-priority leadership role designed for an expert who doesn't just write code but understands the full lifecycle of a data science program—from experimentation to production-grade maintenance. You will act as the bridge between technical execution and executive vision, working closely with our product team of engineers, while growing a dedicated team of AI specialists. You will be responsible for architecting our AI workflows, overseeing their implementation, measuring success through data, consistently improving quality, and communicating progress directly to leadership.
Requirements
- Extensive, hands-on experience developing and deploying applications powered by LLMs (e.g., GPT, Claude, or open-source equivalents)
- Proven track record of taking ML models out of "notebook/experimentation" phases and into a live, scalable production environment
- Experience overseeing the high-level strategy of a data science program, including defining the "common working language" and methodologies for the team
- Formal data science training from a major university to ensure alignment on theoretical principles and industry standards
- Deep experience in Python-driven backend development; ability to architect the logic that supports AI features
- Familiarity with Google Cloud Platform tools for model hosting, data storage, and scaling
- Experience managing the full Experiment, Deploy, Measure lifecycle, specifically using data to prove if a model change improved user outcomes
- Proficiency in gathering, cleaning, and structuring data for specific AI use cases, such as RAG (Retrieval-Augmented Generation) for chat apps
- Experience in a lead or senior role where you were responsible for delegating tasks and mentoring other engineers
- Experience preparing slide decks and status reports for C-suite leadership to explain technical roadmaps and AI efficacy
- Experience working within a multi-disciplinary product team (designers, frontend engineers, etc.) to integrate AI into a seamless user experience
- Specific experience using feedback loops (e.g., human data correction processes, chat feedback) to continuously improve model performance based on real-world behavior
Responsibilities
- Lead the end-to-end lifecycle of ML and Generative AI models, including data structuring, information gathering, prototyping and experimentation, implementation, deployment, and continuous monitoring
- Act in a lead capacity by delegating tasks effectively and overseeing the "experiment-deploy-measure" cycle
- Deploy and maintain sophisticated AI applications (e.g., chat applications, document analysis models) and iterate based on user behavior and feedback
- Prepare decks and reports for the CEO and marketing teams to translate complex technical milestones into clear business updates
- Maintain and scale a Python-based backend, ensuring seamless integration with our Next.js frontend
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
