Engineering Lead ( AI/ML, Gen AI)

‒Design and build AI solutions across a range of business problems, choosing the right approach for each: document and image extraction or classification, predictive models, workflow automation, LLM-based agents and more.

‒Apply AI engineering best practices across every project: prompt design, retrieval strategies, evaluation frameworks, regression testing and version control for models and prompts.

‒Set and enforce model governance standards: approval workflows, risk and bias assessment, and documentation for every model promoted to production.

‒Build observability into every AI system: logging, tracing and monitoring for inputs, outputs, latency and failure modes, so issues surface before they reach the business.

‒Own token economics and compute cost across LLM-based and other AI systems: track cost per request, choose the right model size for each task, and balance accuracy against spend.

‒Build and maintain a dashboard that tracks the metrics that matter, such as accuracy, latency, cost, throughput, drift and error rate, across every AI system in production.

‒Evaluate and select the right AI approach for each problem, whether that is an LLM, a classical machine learning model, computer vision or a mix, based on what the problem actually needs rather than what is fashionable.

‒Mentor engineers on AI engineering practices, and build a shared standard for how the team designs, tests and ships AI systems.

‒Explain what an AI system can and cannot do, in plain terms, to non-technical stakeholders and leadership, so expectations stay realistic.

‒Track new AI and machine learning techniques, and test them against real business needs rather than adopting them for their own sake.

‒8+ years of software engineering experience, including 3+ years focused on applied AI or machine learning in production.

‒Hands-on experience building and deploying a range of AI solutions, such as document or image extraction and classification, predictive models, recommendation systems, or LLM-based agents and assistants.

‒Strong programming skills in Python or a comparable language, and fluency with standard AI and ML tooling: model frameworks, orchestration frameworks and vector databases.

‒Experience building observability into AI systems: logging, tracing, monitoring and alerting for model behavior in production.

‒Experience with model governance: approval workflows, risk and bias assessment, and documentation standards for models moving into production.

‒Working knowledge of token economics and compute cost management for AI systems at scale.

‒Experience building dashboards or metrics systems that track model and system performance over time.

‒A track record of leading or mentoring engineers and setting technical direction, not only contributing as an individual.

‒Strong business acumen: you translate an AI capability into a concrete business outcome, and you know when a simpler, non-AI solution is the right call.

‒Clear communication skills. You explain technical trade-offs to engineers and non-technical stakeholders alike, without losing precision.

‒Experience with document or image classification and extraction, as one of several AI domains you have worked in.

‒Experience with a dashboarding tool such as Power BI, Tableau or Grafana, used for tracking model and system metrics.

‒Familiarity with prompt versioning or evaluation frameworks used for regression testing model outputs.

‒Exposure to a data-intensive industry, such as real estate, financial services or health care.