Overview

The successful candidate will be a respected technical leader who can move comfortably between strategy, architecture, hands-on engineering, and delivery execution.

Qualifications

• Bachelor's degree or higher in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related field.
• 15+ years of technology experience with progressive technical and delivery leadership responsibilities.
• 10+ years of experience across AI, machine learning, natural language processing, data science, or data-intensive software engineering.
• Proven experience designing and deploying production Generative AI, retrieval-augmented generation, or Agentic AI solutions at enterprise scale.
• Experience leading cross-functional and globally distributed engineering teams delivering complex enterprise platforms or products.
• Demonstrated hands-on architecture and engineering depth, including design review, code review, evaluation, security, integration, and production troubleshooting.
• Experience with cloud platforms, enterprise integrations, DevSecOps/MLOps/LLMOps, observability, and production operations.
• Direct experience with Human Capital Management, HR technology, workforce services, talent and career platforms, People/HR shared services, or workforce analytics.
• Ability to communicate effectively with senior business, technology, risk, and governance stakeholders.
• Lead the end-to-end technical delivery of enterprise AI capabilities from discovery and solution design through engineering, testing, deployment, and production stabilization.
• Translate ambiguous business opportunities into clear technical scope, architecture, delivery milestones, measurable outcomes, and an executable engineering backlog.
• Design Agentic AI architectures covering orchestration, routing, planning, context management, memory, knowledge retrieval, tool use, structured outputs, human oversight, and failure recovery.
• Make pragmatic trade-offs across model quality, latency, cost, privacy, security, reliability, maintainability, and time to value.
• Create or review architecture specifications, prototypes, reference implementations, code, prompts, evaluation suites, integration contracts, and operational designs.
• Embed Responsible AI, privacy, security, compliance, traceability, and auditability throughout the solution lifecycle.
• Lead a cross-functional team of AI/ML engineers, software engineers, data scientists, quality engineers, platform engineers, and other technical contributors.
• Own delivery planning, capacity, dependencies, risks, technical quality, release readiness, and stakeholder commitments.
• Establish engineering standards for code quality, testing, CI/CD, DevSecOps, MLOps/LLMOps, infrastructure automation, observability, and production support.
• Ensure solutions are scalable, supportable, cost-aware, and instrumented for performance, quality, safety, and business outcomes.
• Drive reuse through common patterns, shared services, accelerators, engineering playbooks, and platform capabilities.
• Apply strong understanding of Human Capital, HR services, employee and manager journeys, workforce policies, talent processes, and sensitive employment data where relevant.
• Lead large-scale transformation by connecting business strategy, operating processes, data, technology, governance, adoption, and measurable value.
• Communicate architecture, progress, risks, options, and decisions clearly to engineering teams and senior executives.
• Build a high-accountability engineering culture with clear ownership, constructive challenge, rapid learning, and reliable execution.
• Mentor senior engineers and emerging technical leaders through architecture reviews, code reviews, pairing, delegation, and structured feedback.
• Stay current with rapidly evolving models, frameworks, evaluation methods, security risks, regulations, and market practices
• Required Technical Expertise
• Deep expertise in LLM-based applications, Agentic AI systems, copilots, retrieval-augmented generation, prompt and context engineering, structured generation, and tool/function calling.
• Experience with single-agent and multi-agent orchestration, planning, routing, delegation, state, memory, recovery, and bounded autonomy.
• Hands-on experience with chunking, indexing, metadata, hybrid retrieval, reranking, vector databases, knowledge graphs, citation grounding, and authorization-aware retrieval.

Nice to have

• Advanced degree or PhD in a relevant technical discipline.
• Experience with enterprise HR platforms or service ecosystems such as Oracle HCM, Workday, ServiceNow HR Service Delivery, or comparable technologies.
• Experience with corporate governance, model risk, Responsible AI, privacy, security, compliance, internal controls, or audit readiness.
• Forward Deployed Engineering or consulting leadership experience involving rapid problem structuring, hands-on implementation, stakeholder-facing delivery, and transition from prototype to production.
• Experience building reusable AI platforms, orchestration frameworks, evaluation systems, governance tooling, or engineering standards used across multiple teams.
• Experience delivering AI transformation in healthcare, insurance, or another regulated industry.
• Enterprise AI capabilities are delivered with clear scope, strong engineering quality, measurable outcomes, controlled risk, and transparent operational ownership.
• AI solutions meet defined standards for groundedness, safety, security, privacy, reliability, performance, usability, and auditability.
• Reusable patterns and