Supply and Engineering Data & AI Solution Architect

Job description

About Accenture Accenture is a leading global professional services company that helps the world’s leading organizations build their digital core, optimize their operations, accelerate revenue growth and enhance services—creating tangible value at speed and scale. We are a talent- and innovation-led company with 774,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. Visit us at www.accenture.com As an experienced Supply and Engineering Data & AI Solution Architect, you play a pivotal role in shaping and delivering AI solutioning for asset-intensive clients — mining, oil and gas, utilities, chemicals, heavy transport, defence, critical infrastructure, and capital projects. Operating at the intersection of industry domain, client engagement, and engineering, you own the end-to-end solution path from discovery and competitive pursuit through architecture and delivery shaping. You design advanced AI systems that combine agentic architectures with classical machine learning, optimisation, and constraint-based decision techniques, purposefully mapped to how the client's operation actually works. Soft skills and industry fluency are load-bearing: you are equally credible with executives, domain engineers, and technical teams. You can provide the technical expertise and collaboration to translate frontier technology deployments into quantified business value cases regularly connecting technical architecture to COO/CFO-level investment narratives. You understand what it takes to make technology stay operational after go-live, including tailoring operating model and organisational change capabilities as appropriate for solutions to be effective in an organization. You also lead and mentor people by default - growing capability, standards, compelling visions and reusable assets throughout your ecosystem. THE WORK Lead client discovery, executive briefings, and technical deep-dives — translating the same solution across C-suite, operations, engineering, and IT architecture audiences without losing substance. Facilitate stakeholder workshops to surface pain, align feasibility, prioritise use cases, and leave a clear path to pilot or mobilisation. Shape pursuits and proposals (RFI/RFP, competitive tenders, solution plans): overlay proven reference patterns onto the client's named systems, processes, vocabulary, and quantified pain — never present a generic case as if it were theirs. Translate operational and commercial strategy into a technical vision, defining functional and non-functional requirements for performance, reliability, auditability, safety, and cost. In collaboration with your colleagues, translate your technical visions back into quantified business value cases for C-suite and executive audiences, connecting investment to measurable operational outcomes. Architect multi-agent systems (orchestration, tool-calling, plan-and-execute, grounding, evaluation) with explicit human-in-the-loop and audit trails where operators must retain control. Combine agentic techniques with classical methods where they belong: LP/MIP and operations-research optimisers, constraint solvers, forecasting and classical ML, simulation, knowledge graphs, and retrieval-augmented generation. Define the seams between agents, optimisers, and human decision-makers — when the system decides, when it recommends, and when a coordinator or engineer must approve. Drive build-vs-buy 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 modelling — spanning cloud, edge, and OT environments including historian systems, industrial data architectures, and the data latency and protocol constraints (OPC-UA, Modbus, DNP3) that govern real-time operational technology. Elicit and codify hard, soft, and dynamic operational constraints with domain SMEs until plans and recommendations are executable in the real operation. Frame coupled, multi-domain decision problems as connected systems (for example yard ↔ rail ↔ crew, or commercial demand ↔ marine/ops supply) rather than siloed point tools. Separate what is proven, assumed, and still to be confirmed; rebuild value cases on the client's own data and pain, not on reference-client results. Produce and own authoritative solution artefacts: architecture blueprints, decision frameworks, overlay crosswalks, sequence designs, and Architectural Decision Records (ADRs). Independently design and deliver proof-of-concept prototypes that validate architectural decisions with clients and delivery teams. Drive frontier technology from pilot to scaled operational deployment — collaboratively defining post-go-live operating models, governance structures, and workforce capability requirements that ensure AI and digital systems remain operational and continue delivering value after handover. Ensure consideration and coordination with auxiliary Accenture teams to design and deliver solutions for sovereign, regulated, or restricted-access environments — including air-gapped deployments, OT cybersecurity architecture, and data sovereignty requirements for Defence, critical infrastructure, and government clients. Integrate autonomous systems, robotics, and physical AI into industrial solution architectures — understanding how AI decisions translate into physical action in field, plant, and asset environments, and designing the control, safety, and audit layers that responsible deployment requires. Operate as a capability lead within a matrixed, multi-discipline practice — maintaining active interlocks with digital engineering, value consulting, and industry go-to-market teams, and contributing to integrated solutions that span capability domains rather than individual offerings. Lead and mentor cross-functional Supply Chain and Engineering Data & AI teams (data, engineering, industry SMEs, change) on architectural intent, client standards of evidence, and engagement craft. Build and grow the Supply Chain and Engineering Data & AI offering: develop capabilities, coach practitioners, and contribute reusable solution patterns and reference architectures back into the practice. Continuously research and integrate emerging agentic and decision-intelligence patterns while retaining judgement on when classical techniques remain the better tool. EDUCATION Bachelor's Degree or equivalent (Engineering, Physical Sciences, Computer Science, Operations Research, or related). Advanced degree preferred. BASIC (REQUIRED) QUALIFICATION 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 e