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.
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