Lead Software Engineer (Skills - Gen AI, LLM, Python, Lean Six Sigma)

Role summary

The Lead AI Process Transformation Engineer leads larger, complex and cross-functional workflow redesign and AI-enabled transformation opportunities. The role is the senior process-and-AI engineering anchor for OEE, combining enterprise process transformation, capability and maturity assessment, portfolio leadership, practical solution architecture, hands-on technical credibility, executive influence and coaching.

Role purpose and scope

This role operates within OEEs process-first transformation model. It owns the path from ambiguous enterprise problem to validated current state, redesigned workflow, bounded AI-enabled solution, governance approval, adoption and realized value. The role leads the second AI Process Engineer, supports the AI Builders Network and works across business and technology teams to ensure that technical choices follow process evidence rather than lead it.

Enterprise workflow transformation leadership

• Lead complex, cross-functional workflow discovery across people, process, technology, data, policies, controls, systems and organizational boundaries.
• Frame ambiguous business challenges into clear outcomes, process boundaries, operating assumptions, measures and decision requirements.
• Guide current-state and future-state design using process maps, SIPOCs, value-stream views, service blueprints, decision flows and operating-model views.
• Challenge solution-first requests and ensure the sequence Eliminate, Simplify, Standardize, Integrate, then Automate or apply AI is followed.
• Define process ownership, roles, decision rights, controls, exception paths, escalation routes, service expectations and management routines.
• Review and assure the quality of transformation charters, process designs, value cases, implementation plans and control approaches.

Capability mapping and transformation portfolio leadership

• Lead capability-mapping and maturity-assessment activities linking enterprise outcomes to capabilities, workflows, owners, KPIs, systems, data and dependencies.
• Build and govern a prioritized pipeline of workflow redesign and AI-enabled transformation opportunities.
• Assess opportunities using strategic alignment, customer impact, volume, effort, cycle time, quality, risk, controls, data readiness, feasibility, reuse and value potential.
• Establish decision gates for discovery, redesign, prototype, pilot, production handoff, scale, remediation and retirement.
• Manage dependencies, risks, decisions and capacity across multiple transformation opportunities and technical partners.
• Provide concise executive views of portfolio health, value, risks, readiness and decisions required.

AI solution architecture and technical direction

• Translate redesigned workflows into AI-enabled solution strategies covering tasks, inputs, outputs, context, knowledge, integrations, controls, human review and operational ownership.
• Define solution patterns for agentic workflows, AI assistants, retrieval-based solutions, workflow automation and deterministic controls.
• Lead or co-lead proofs of concept and bounded implementations using enterprise-approved platforms such as Microsoft Copilot Studio, Power Automate, Azure AI capabilities, approved APIs and Python.
• Provide technical direction on prompt engineering, grounding, retrieval-augmented generation, orchestration, evaluation and integration patterns.
• Determine when bounded low-code or coded implementation is appropriate and when production engineering, architecture or data-platform support is required.
• Assure that solution design includes human-in-the-loop decisions, exception handling, fallback, access controls, auditability, supportability and lifecycle ownership.

AI Builders Network and team leadership

• Act as the senior process-transformation partner for Principal AI Leads, AI Builders and business leaders on complex or cross-functional opportunities.
• Coach the second AI Process Engineer and builders on workflow discovery, process-first solutioning, value measurement, agent design, testing and adoption.
• Establish reusable playbooks, templates, prompts, test packs, control patterns and solution assets for enterprise reuse.
• Conduct design reviews and provide escalation support where process, technology, risk or ownership choices are unclear.
• Build practical process-transformation and AI fluency across OEE and participating business teams.

Value realization, governance and adoption

• Own the measurement strategy across baselines, targets, leading indicators, lagging indicators and balancing measures.
• Develop and challenge value cases covering financial savings, cost avoidance, capacity release, cycle time, quality, risk, service and customer or associate value.
• Ensure forecast and realized value are validated with appropriate business and Finance partners.
• Lead governance readiness across Responsible AI, Architecture, Information Security, Privacy, Legal, Risk, Accessibility and Records requirements.
• Guide adoption, stabilization, operational handover and scale, remediate or retire decisions.
• Ensure accountable business owners retain ownership for workflow decisions, adoption and sustained outcomes.

Required qualifications and experience

• Bachelor's or Master's degree in Engineering, Computer Science, Information Systems, Data, Business, Operations or a related field, or equivalent practical experience.
• 10+ years of relevant experience, including significant leadership across process transformation, business architecture, solution engineering, automation or AI-enabled delivery.
• Demonstrated experience independently leading complex cross-functional current-state discovery, future-state redesign and implementation.
• Experience in capability mapping, maturity assessment, transformation portfolio prioritization and executive decision support.
• Strong practical Lean and process-engineering capability covering process mapping, SIPOC, VSM, waste analysis, root-cause analysis, standard work, controls and benefit measurement.
• Hands-on credibility building or co-building AI agents, intelligent workflows, automation prototypes or small digital solutions on enterprise platforms.
• Strong working knowledge of generative AI, LLMs, agentic workflows, RAG, prompt engineering, grounding, orchestration and solution evaluation.
• Ability to work with APIs, structured data and scripting; working proficiency in Python or an equivalent language.
• Demonstrated ability to coach practitioners, influence senior leaders, challenge solution-first requests and drive outcomes without direct authority.
• Excellent executive communication and ability to convert complex evidence into clear choices, trade-offs and decisions.

Preferred qualifications and experience

• Lean Six Sigma Black Belt, or equivalent advanced process-transformation qualification supported by practical application.
• Experience with Microsoft Copilot Studio, Power Automate, Azure AI Foundry, Azure OpenAI, Semantic Kernel or comparable platforms.
• Working knowledge of API integration, JSON, authentication, connectors, data preparation, vector search and enterprise knowledge sources.
• Experience leading or supporting an AI Center of Excellence, Builders Network, transformation studio or enterprise automation program.
• Experience with Agile delivery, product thinking, service design, organizational change, operating-model design or program management.
• Experience in Finance, Operations, Technology, shared services, customer operations or another complex enterprise environment.
• Cloud, AI, business architecture, process-improvement or automation certifications.

Expected outcomes

• A governed portfolio of high-value workflow redesign and AI-enabled transformation opportunities with clear sequencing, owners and decision gates.
• Priority capabilities and workflows have validated current states, maturity gaps, baselines, future states, controls and readiness decisions.
• Complex opportunities progress from discovery through redesign, bounded solution, governance, adoption and measurable value realization.
• AI Builders and the second AI Process Engineer receive effective technical and process-transformation direction.
• Reusable process, solution, testing and control patterns are adopted across multiple opportunities.
• Leadership receives transparent and decision-ready evidence on value, risk, dependencies, adoption and scale.

What success looks like

• Creates clarity and a credible path forward from ambiguous enterprise problems.
• Improves the complete workflow and operating mechanism, not merely one automated task.
• Balances process rigor, technical feasibility, control requirements, speed and business value.
• Can challenge leaders constructively and guide multiple stakeholders toward an evidence-based decision.
• Builds team and enterprise capability while personally retaining technical and transformation credibility.
• Delivers sustained outcomes and reusable transformation capability.