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

Role summary

The Senior AI Process Transformation Engineer provides delivery capacity for workflow redesign and AI-enabled transformation. The role combines practical process discovery and redesign with hands-on implementation of bounded agents, intelligent workflows, automation prototypes and small digital solutions, while partnering with the Lead AI Process Transformation Engineer on complex or enterprise-wide opportunities.

Role purpose and scope

This role operates within OEEs process-first transformation model. It moves assigned opportunities from business problem and evidence through current-state discovery, future-state redesign, functional and technical requirements, prototype or small implementation, testing, adoption and value tracking. The role is expected to execute independently within an agreed scope and escalate architecture, risk or enterprise decisions with clear evidence.

Workflow discovery and process redesign

• Partner with process owners and subject-matter experts to define the business outcome, customer need, scope, process boundary and measures of success.
• Conduct current-state discovery across people, process, technology, data, policies, controls, systems and handoffs.
• Create current-state and future-state process maps, SIPOCs, value-stream views, service blueprints and decision flows.
• Identify waste, delays, queues, rework, duplicate activity, failure demand, inconsistent rules, control gaps and avoidable variation.
• Facilitate focused root-cause analysis using Problem Trees, 5 Whys, Fishbone, Pareto and targeted data analysis.
• Design future-state workflows using Eliminate, Simplify, Standardize, Integrate, then Automate or apply AI.
• Document process owners, roles, controls, exceptions, escalation paths, service expectations and operational measures.

Capability evidence and opportunity assessment

• Connect assigned workflows to relevant capabilities, outcomes, owners, KPIs, systems, data and dependencies.
• Prepare evidence, workflow inventories and assessment views to support capability mapping and maturity assessment.
• Assess opportunities using volume, effort, cycle time, quality, customer impact, risk, data readiness, feasibility and value potential.
• Distinguish process problems from technology problems and flag solution requests where ownership, baseline, controls or expected outcomes are unclear.
• Create transformation charters, use-case definitions, implementation backlogs, testable value hypotheses and decision packs.

Applied AI solution engineering

• Translate redesigned workflows into functional and technical requirements covering tasks, inputs, outputs, decision rules, context, knowledge, integrations, controls and human review.
• Design and build or co-build bounded agents, AI assistants, retrieval-based solutions and workflow automations using approved platforms and patterns.
• Create proofs of concept and small implementations using Microsoft Copilot Studio, Power Automate, Azure AI capabilities, approved APIs and Python where appropriate.
• Apply prompt engineering, grounding, RAG, orchestration and evaluation techniques suitable for the use case.
• Configure human-in-the-loop decisions, exception handling, fallback behavior, auditability and access controls.
• Create and execute test cases covering business accuracy, groundedness, usability, process performance, controls and failure conditions.
• Partner with Lead Engineer, Engineering and Architecture where production software, complex integration or enterprise data engineering is required.

Builders Network delivery support

• Support Principal AI Leads and AI Builders in translating business problems and backlogs into process-ready use cases.
• Facilitate workflow discovery and identify what should be removed, simplified or standardized before agent development.
• Provide hands-on guidance on prompt structure, grounding, testing, controls, adoption and value measurement.
• Document and publish reusable workflows, prompts, test scripts, control approaches and solution assets.
• Escalate complex, high-risk or cross-functional choices to the Lead AI Process Transformation Engineer and the appropriate Responsible AI, Architecture, Information Security, Privacy, Legal and Risk partners.

Measurement, adoption and sustainment

• Establish operational definitions, baselines, targets, leading indicators, lagging indicators and balancing measures for assigned opportunities.
• Prepare value cases covering capacity, cycle time, quality, risk, service and customer or associate value.
• Track adoption, usage, performance, reliability, exceptions, control results and forecast versus realized value.
• Support change, training, operational handover and stabilization activities.
• Provide clear status updates on progress, risks, dependencies, decisions and outcomes.

Required qualifications and experience

• Bachelor's degree in Engineering, Computer Science, Information Systems, Data, Business, Operations or a related field, or equivalent practical experience.
• 5+ years of relevant experience spanning process improvement, business analysis, workflow transformation, automation, solution engineering or AI-enabled delivery.
• Demonstrated experience conducting cross-functional current-state discovery and contributing to future-state workflow redesign.
• Practical Lean knowledge covering process mapping, SIPOC, waste analysis, root-cause analysis, standard work, controls and benefit measurement.
• Hands-on experience building AI agents, intelligent workflows, automation prototypes or small digital solutions using at least one enterprise platform.
• Working knowledge of generative AI, LLMs, agentic workflows, RAG, prompt engineering, knowledge grounding and solution evaluation.
• Ability to translate business and process requirements into functional designs, technical requirements, test cases and implementation tasks.
• Ability to work with APIs, structured data and basic scripting; working proficiency in Python or an equivalent language.
• Strong facilitation, analytical problem-solving, stakeholder management and written and verbal communication.
• Ability to work through ambiguity, manage assigned work independently and escalate decisions with clear evidence.

Preferred qualifications and experience

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

Expected outcomes

• Assigned workflows have validated current states, owners, baselines, target outcomes, controls and documented readiness decisions.
• Prioritized opportunities progress efficiently from discovery to redesigned workflow, bounded prototype or implementation, adoption and measurable outcome.
• Builders receive practical process and solution-engineering support for assigned opportunities.
• Prototypes and small implementations demonstrate value without creating unmanaged production risk.
• Reusable process, prompt, testing and control assets are captured and published.
• Business owners receive clear evidence on performance, adoption, risks and realized value.

What success looks like

• Diagnoses the workflow before prescribing technology.
• Produces accurate, usable process, requirement, test and implementation artifacts.
• Builds enough of the solution to accelerate learning and delivery while respecting production boundaries.
• Escalates evidence-based choices early and collaborates effectively with business and technical specialists.
• Delivers adopted and controlled improvements with measurable outcomes.
• Continually builds proficiency in both process transformation and applied AI engineering.