Supply and Engineering Data & AI Solution Architect
Technology, Data & Digital · Data, AI & Analytics · Data Science · Software Engineering
In short
Accenture is seeking an experienced Supply and Engineering Data & AI Solution Architect in Perth to lead AI solutioning for asset-intensive clients. You will design advanced AI systems combining agentic architectures with classical machine learning and optimization techniques, translating technical visions into quantified business value for C-suite audiences.
Responsibilities
- Lead client discovery, executive briefings, and technical deep-dives, tailoring communication across C-suite, operations, engineering, and IT audiences.
- Facilitate stakeholder workshops to identify pain points, align on feasibility, prioritize use cases, and define clear paths to pilot or mobilization.
- Shape pursuits and proposals (RFI/RFP, competitive tenders, solution plans) by integrating proven reference patterns with client-specific systems, processes, and pain points.
- Translate operational and commercial strategy into technical visions, defining functional and non-functional requirements for performance, reliability, auditability, safety, and cost.
- Collaborate with colleagues to translate technical visions into quantified business value cases for C-suite and executive audiences, linking investment to measurable operational outcomes.
- Architect multi-agent systems with explicit human-in-the-loop and audit trails where operators retain control.
- Combine agentic techniques with classical methods such as LP/MIP, operations research optimizers, constraint solvers, forecasting, classical ML, simulation, knowledge graphs, and retrieval-augmented generation.
- Define clear boundaries between agents, optimizers, and human decision-makers, specifying when the system decides, recommends, or requires approval.
- Drive build-vs-buy decisions and technology selection for AI and decision platforms.
- Architect end-to-end data and context layers for industrial and supply-chain environments, including grounded retrieval and knowledge modeling across cloud, edge, and OT environments.
- Elicit and codify operational constraints with domain SMEs to ensure plans and recommendations are executable in real operations.
- Frame coupled, multi-domain decision problems as connected systems rather than siloed point tools.
- Produce and own authoritative solution artifacts such as architecture blueprints, decision frameworks, overlay crosswalks, sequence designs, and Architectural Decision Records (ADRs).
- Independently design and deliver proof-of-concept prototypes to validate architectural decisions with clients and delivery teams.
- Drive frontier technology from pilot to scaled operational deployment, defining post-go-live operating models, governance, and workforce capability requirements.
- Ensure consideration and coordination with auxiliary Accenture teams for solutions in sovereign, regulated, or restricted-access environments.
- Integrate autonomous systems, robotics, and physical AI into industrial solution architectures, designing control, safety, and audit layers.
- Operate as a capability lead within a matrixed practice, maintaining interlocks with digital engineering, value consulting, and industry go-to-market teams.
- Lead and mentor cross-functional Supply Chain and Engineering Data & AI teams on architectural intent, client standards of evidence, and engagement craft.
- Build and grow the Supply Chain and Engineering Data & AI offering by developing capabilities, coaching practitioners, and contributing reusable solution patterns and reference architectures.
- Continuously research and integrate emerging agentic and decision-intelligence patterns while retaining judgment on the suitability of classical techniques.
Requirements
- Minimum of 8 years of experience in industry, consulting, or enterprise delivery within asset-intensive, supply-chain, or complex engineering/operations environments.
- Minimum of 4 years of experience designing and delivering enterprise-grade AI solutions spanning agentic, generative, and classical AI/ML — using at least one major cloud vendor (AWS, Azure, or GCP).
- Minimum of 3 years of experience in the agentic, LLM, and generative AI space, including orchestration patterns and human-in-the-loop design.
- Minimum of 3 years of experience combining AI with optimisation, operations research, constraint modelling, or classical ML for operational or commercial decision systems.
- Demonstrated client-facing experience leading discovery, technical deep-dives, and executive conversations with the ability to change register across stakeholder levels, including C-suite audiences where the conversation centres on business value and investment justification.
- Demonstrated experience shaping pursuits or proposals (RFI/RFP responses, solution architecture for competitive bids, or equivalent).
- Deep domain fluency in one or more of: mining, oil and gas, utilities, chemicals, heavy industry, capital projects, Defence or critical infrastructure, or complex supply-chain / engineering operations — sufficient to diagnose the real problem, not only the stated ask.
- Minimum of 4 years of hands-on technical experience (e.g. Python and/or solution prototyping) sufficient to validate architecture decisions and guide engineering teams.
- Demonstrated experience leading and mentoring teams, and contributing to offering or capability development within a Data & AI, supply chain, or engineering practice.
- Demonstrated experience deploying or architecting AI solutions in operational technology (OT) or industrial edge environments — including familiarity with historian systems, industrial data architectures, and the constraints of real-time OT integration.
Desired Qualifications
- Bachelor's Degree or equivalent (Engineering, Physical Sciences, Computer Science, Operations Research, or related). Advanced degree preferred.
- Background that trained systems thinking under uncertainty (e.g. engineering, geophysics/reservoir, process, reliability, OR) applied to AI solutioning.
- Experience designing knowledge/document intelligence or knowledge-graph solutions for technical corpora.
- Prior work on scheduling, planning, maintenance, production, logistics, marine/commercial supply chain, or asset performance decision systems.
- Experience building reusable IP, reference architectures, or practice assets that scale an offering beyond a single engagement.
- Awareness of designing for sovereign, regulated, or security-classified environments — including air-gapped deployments, OT cybersecurity, or data sovereignty requirements relevant to Defence, critical infrastructure, or government clients.
- Familiarity with autonomous systems, robotics integration, or physical AI deployment patterns in industrial or field environments — particularly the safety, control, and audit architecture required for responsible physical AI deployment.
- Experience taking AI or advanced analytics solutions from pilot through to scaled operational deployment, including the operating model, governance, and workforce capability components — not only the technical implementation.
- Experience operating as a capability or domain lead in a matrixed consulting practice — collaborating across service lines (e.g. digital engineering, value consulting, systems integration) to shape and deliver integrated multi-capability solutions.
Benefits
- Flexible working
- Career development opportunities
- Access to a global ecosystem of leading technologies and clients
- Progressive Employee Benefits covering Health, Wellbeing, Progressive Leave Options, Sustainability
#Data & AI#Solution Architect#Supply Chain#Engineering#Asset Intensive#Mining#Oil and Gas#Utilities#Chemicals#Heavy Transport#Defence#Critical Infrastructure#Capital Projects#Machine Learning#Optimization#Constraint-based Decision#Agentic Architectures#Multi-agent Systems#Generative AI#LLM#Knowledge Graphs#Retrieval Augmented Generation#OT Integration#Industrial Data Architecture#Real-time Operational Technology