Engineering Lead ( AI/ML, Gen AI)

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

‒         Apply AI engineering bestpractices across every project: prompt design, retrieval strategies, evaluationframeworks, regression testing and version control for models and prompts.

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

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

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

‒         Build and maintain a dashboardthat 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 rightAI approach for each problem, whether that is an LLM, a classical machinelearning model, computer vision or a mix, based on what the problem actuallyneeds rather than what is fashionable.

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

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

‒         Track new AI and machinelearning techniques, and test them against real business needs rather thanadopting them for their own sake.

What You Bring

‒         8+ years of softwareengineering experience, including 3+ years focused on applied AI or machinelearning in production.

‒         Hands-on experience buildingand deploying a range of AI solutions, such as document or image extraction andclassification, predictive models, recommendation systems, or LLM-based agentsand assistants.

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

‒         Experience buildingobservability into AI systems: logging, tracing, monitoring and alerting formodel behavior in production.

‒         Experience with modelgovernance: approval workflows, risk and bias assessment, and documentationstandards for models moving into production.

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

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

‒         A track record of leading ormentoring engineers and setting technical direction, not only contributing asan individual.

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

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

Nice to Have

‒         Experience with document orimage classification and extraction, as one of several AI domains you haveworked in.

‒         Experience with a dashboardingtool such as Power BI, Tableau or Grafana, used for tracking model and systemmetrics.

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

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