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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.

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

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

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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.

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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.

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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.

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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.

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

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

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

What You Bring

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

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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.

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

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

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

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

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

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

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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.

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

Nice to Have

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

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

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

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