Accenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. Our strategy is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client-focused, AI-enabled, great place to work in the world. We bring together the talent of our approximately 814,000 people with proprietary assets and platforms, deep process and industry expertise, and leading ecosystem relationships to deliver end-to-end solutions and measurable outcomes at scale. Through our Reinvention Services, we offer broad expertise across Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology. We serve approximately 9,000 clients and generated approximately $74 billion in FY26 revenue. Visit us at accenture.com .
Our Team:
QuantAI sits between quantitative research, agentic engineering, product delivery, and client-facing transformation inside Accenture's Industry and Enterprise Reinvention aimed at servicing the CEO function. The work is small-team, high-ownership, and close to senior stakeholders.
QuantAI is building artificial intelligence (AI)-native decision systems for energy, commodities, power, utilities, trading, financial, and industrial operations. The quantitative foundation is already strong. The next bottleneck is turning that foundation into enterprise-ready products: useful front ends, reliable backend services, practical deployment paths, reusable architecture, and the engineering discipline needed to move from demo to pilot to repeatable client offering.
- The Director of QuantAI Research and Rapid Prototyping will continue to set and drive the research and strategic direction informed by inputs from senior leaders and client stakeholders. This role is expected to spearhead the full-stack execution layer: product architecture, build quality, deployment path, engineering practices, and the growth of the full-stack team.
- The team already brings deep experience in research, forecasting, and market modeling. The full-stack practice is newer, giving this manager meaningful room to help shape how it grows.
- The team is building reusable assets that can move from internal demo to client pilot to repeatable offering. The goal is not a collection of disconnected proofs of concept.
- The first commercial wedge is centered on energy, commodities, power, utilities, trading, and industrial decision systems, with adjacent financial workflows where the fit is real.
- You should expect direct technical feedback, ambiguous problem statements, fast iteration, and close collaboration with quants, engineers, client teams, and senior leaders.
- This is a strong fit for someone who likes building in the open, using agentic tools to move faster, and still taking responsibility for whether the product is secure, usable, reliable, explainable, and worth shipping.
Your Role
We are seeking a hands-on full-stack productization manager who can spearhead that layer. You will work closely with the Director of QuantAI Research and Rapid Prototyping; the ideal candidate for this role will translate that direction into product architecture, working software, deployment choices, engineering standards, and a full-stack team built around the work. You would be the first full-stack manager on this team, with the opportunity to help shape the practice from the start: setting thoughtful standards, building alongside the people you hire, and creating the environment where the team can do its best work. This role may be a good fit for a builder-manager who has shipped real applications, worked through ambiguity in a startup-like environment, and knows how to make sound technical trade-offs when the answer is not yet obvious.
This is not a generic delivery manager, a pure advisory role, or a role that delegates the build while staying above the details. It fits the candidate who can create structure in a startup-like environment with access to resources from global professional services organization, stay close to code, architecture and infrastructure, build attractive and effective interfaces for expert users, and treat enterprise constraints as part of the design rather than an afterthought.
You do not need to be steeped in quantitative research, but you do need enough quantitative and AI and agentic fluency to work credibly with the team.
Key Responsibilities:
- Spearhead the full-stack productization path for QuantAI assets, turning quantitative and agentic prototypes into applications, interfaces, workflow tools, services, and packaged products that can hold up with internal senior leaders, expert users, and client stakeholders.
- Stand up frontend experiences that make advanced algorithms usable in real workflows, including dashboards, expert-facing application flows, evaluation views, governance interfaces, and decision-support surfaces for trading, energy, utilities, industrial, and adjacent financial use cases. You will have room to shape these interfaces around the needs of expert users, balancing information density, clear hierarchy, and fast access to the decisions and numbers that matter.
- Make pragmatic architecture choices across frontend, backend, application programming interfaces (APIs), data flows, model-serving surfaces, agentic orchestration, and evaluation infrastructure.
- Decide when a solution should be cloud-hosted, client-hosted, locally packaged, desktop-first, or hybrid, and navigate the trade-offs across security, data access, user workflow, latency, cost, and enterprise approval paths.
- Research and evaluate internal Accenture capabilities, client technology constraints, commercial platforms, and open-source tooling, then choose the simplest path that can move fast without creating fragile or non-compliant systems.
- Build, review, and unblock implementation across Python, TypeScript or JavaScript, modern web frameworks, such as React, Next.js, or Angular,APIs, data services, and deployment workflows.
- Establish product engineering discipline around authentication, role-based access control (RBAC), observability, security, release quality, continuous integration and continuous delivery (CI/CD), cost controls, and regression testing.
- Design the agentic systems that help build and run our products, not just speed up coding: coordinate multiple agents, match each step to an appropriate model, add checks before outputs reach users, support long-running work that can resume reliably, preserve decision context in the repository, and make thoughtful cost and governance choices. Human judgment, code review, security, testing, and architecture decisions stay firmly in the loop.
- Build the evaluation layer that shows how the work is performing. Define success before the build starts, use checks that are as objective and repeatable as the work allows, and make drift visible early through reference outputs, regression cases, accuracy, latency and cost benchmarks, and checks for unsupported language-model output.
- Help each build inform the next by turning successful patterns into shared components, internal tooling, templates, and generators, making related products faster and easier to deliver over time.
- Work closely with the Director of QuantAI Research, quants, fullstack engineers, strategists and client-facing teams so model logic, evaluation intent, governance requirements, and user workflow all survive and thrive through the move from algorithms into products.
- Help grow the full-stack side of the team by defining job-related hiring criteria, contributing to structured technical assessments, coaching full stack engineers and setting clear, consistent technical .
- Work Environment (Hybrid Expectations): Travel may be required based on project needs. Flexibility to work remotely when not on-site with clients or team. Note: Project assignments may require variability in schedule and location.
Here’s what you need
- Bachelor's degree in computer science, engineering, mathematics, physics, economics, finance, operations research, or a related field, plus a record of software you have shipped and supported end to end, including at least one product, platform, internal tool, or AI or machine learning system that reached real users and moved beyond an initial prototype.
- Minimum 5 years of relevant experience building software (We care most about the relevance of your experience and the work you have delivered.)
- Minimum of 2 years of hands-on depth across full-stack delivery: modern front-end work, backend services or APIs, databases or data flows, deployment paths, and practical debugging across several layers of the stack. Experience with all or any of the following tools: Python, TypeScript, React, Next.js or Angular, FastAPI, Postgres, Docker, cloud services such as AWS or Azure, Electron or Tauri for packaged desktop delivery, OpenAPI contracts, CI/CD systems, end-to-end browser testing, observability tooling, model-serving infrastructure, or evaluation frameworks.
Bonus points if you have:
- Strong product and architecture judgment in ambiguous environments: you can turn unclear needs into scoped releases, choose between hosted, desktop, hybrid, internal, external, commercial, and open-source options, and explain the trade-offs clearly and make interface decisions that work well for expert users in complex settings.
- Evidence that you can operate as a hands-on builder-manager: review technical work thoughtfully , unblock implementation, coach or manage engineers, contribute to structured and job-related hiring decisions, communicate with senior stakeholders, and maintain clear expectations while staying close to the work.
- Experience designing AI or agentic systems, rather than only working alongside them, covering several of: large language model (LLM) application patterns, multi-agent orchestration, retrieval, tool use, memory, guardrails, human-in-the-loop gating, and the evaluation harnesses that show how these systems behave. You should be able to explain a system's current limits and how you would test its important assumptions.
- Experience in energy, power, utilities, commodities, trading, market operations, industrial systems, financial workflows, or other high-stakes decision-support environments.
- Experience in startup, zero-to-one product, platform engineering, developer tooling, enterprise software, Windows-heavy deployment, or consulting delivery environments where priorities shift and judgment matters more than a perfect brief.
- Examples that help us understand how you build, such as open-source contributions, published tooling, technical writing, competition or benchmark results, or work samples you can discuss without sharing confidential information. Public work is welcome but not required.Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.We anticipate this job posting will be posted until 11/20/2026.Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
U.S. Employee Benefits | Accenture (https://www.accenture.com/us-en/careers/local/benefits)
Role Location Annual Salary RangeCalifornia $87,400 to $253,000Colorado $87,400 to $253,000Connecticut $87,400 to $253,000District of Columbia $87,400 to $253,000Illinois $87,400 to $253,000Maine $87,400 to $253,000Maryland $87,400 to $253,000Massachusetts $87,400 to $253,000Minnesota $87,400 to $253,000New York $87,400 to $253,000New Jersey $87,400 to $253,000Ohio $87,400 to $253,000Virginia $87,400 to $253,000Washington $87,400 to $253,000
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