Trading, Investment & Optimization - QuantAI Full Stack Manager (Hybrid)

Quant AI 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.

Quant AI 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 Quant AI Research and Rapid Prototyping will set and drive the research and strategic direction, spearhead full‑stack execution, product architecture, build quality, deployment path, engineering practices, and grow the full‑stack team.
• The team builds reusable assets that move from internal demo to client pilot to repeatable offering, not a collection of disconnected proofs of concept.
• The first commercial wedge is energy, commodities, power, utilities, trading, and industrial decision systems, with adjacent financial workflows where the fit is real.
• Expect direct technical feedback, ambiguous problem statements, fast iteration, and close collaboration with quants, engineers, client teams, and senior leaders.
• Strong fit for someone who likes building in the open, using agentic tools to move faster, yet takes responsibility for product security, usability, reliability, explainability, and shipping value.

We are seeking a hands‑on full‑stack productization manager who translates the Director’s direction into product architecture, working software, deployment choices, engineering standards, and builds an elite full‑stack team.

This builder‑manager role requires real application delivery experience, startup‑like ambiguity navigation, and the ability to make right technical trade‑offs.

This is not a generic delivery manager or pure advisory role.

It fits a candidate who creates structure in a startup‑like environment while staying close to code, architecture, infrastructure, and enterprise constraints from the design stage.

Technical fluency in quantitative research, AI, and agentic tools is required to credibly work with the team, but deep domain expertise is not mandatory.

Key Responsibilities

• Spearhead the full‑stack productization path for Quant AI assets, turning prototypes into applications, interfaces, workflow tools, services, and packaged products that satisfy senior leaders, expert users, and client stakeholders.
• Stand up front‑end experiences that make advanced algorithms usable: dashboards, expert‑facing flows, evaluation views, governance interfaces, and decision‑support surfaces for trading, energy, utilities, industrial, and adjacent financial use cases.
• Make pragmatic architecture choices across front‑end, back‑end, APIs, data flows, model‑serving surfaces, agentic orchestration, and evaluation infrastructure.
• Decide hosting options—cloud, client, local, desktop‑first, or hybrid—navigating security, data access, user workflow, latency, cost, and enterprise approval paths.
• Research and evaluate internal Accenture capabilities, client constraints, commercial platforms, and open‑source tooling, then choose the simplest path that moves fast without creating fragile or non‑compliant systems.
• Build, review, and unblock implementation across Python, Type Script or Java Script, modern web frameworks, APIs, data services, and deployment workflows.
• Establish product engineering discipline around authentication, role‑based access control, observability, security, release quality, CI/CD, cost controls, regression testing, and evaluation of AI and agentic behavior.
• Use agentic development tools such as Codex and Claude Code to accelerate delivery while keeping human judgment, code review, security, testing, and architecture decisions in the loop.
• Work closely with the Director of Quant AI Research, quants, full‑stack engineers, strategists, and client‑facing teams to ensure model logic, evaluation intent, governance requirements, and user workflow survive the move from algorithms to products.
• Help build the full‑stack side of the team by shaping hiring signals, coaching engineers, setting technical standards, and creating repeatable build patterns.
• Work Environment (Hybrid Expectations): Travel may be required. Flexibility to work remotely when not on‑site with clients or the team.
• Here’s what you need
• Bachelor's degree in computer science, engineering, mathematics, physics, economics, finance, operations research, or a related field, plus a strong record of shipped software.
• Minimum 5 years of relevant experience building and shipping full‑stack applications, workflow products, internal tools, platforms, AI or machine learning products, or other software systems that moved beyond proof‑of‑concept stage.
• Minimum of 2 years of hands‑on depth across full‑stack delivery: modern front‑end work, back‑end services or APIs, databases or data flows, deployment paths, and practical debugging across several layers of the stack.
• Bonus points if you have
• Strong product and architecture judgment in ambiguous environments: turning unclear needs into scoped releases, choosing hosting options, and explaining trade‑offs clearly.
• Evidence of hands‑on builder‑manager experience: reviewing technical work, unblocking implementation, coaching or managing engineers, hiring strong builders, communicating with senior stakeholders, and maintaining a high bar.
• Experience with tools and platforms such as React, Next. js, Type Script, Tailwind, Vite, Vercel, Python, Fast API, Electron, Tauri, Docker, cloud services, Kubernetes, CI/CD systems, observability tooling, model‑serving infrastructure, or evaluation frameworks.
• Experience building AI or agentic systems: LLM application patterns, agent orchestration, retrieval, tool use, memory, evaluation harnesses, guardrails, or human‑in‑the‑loop workflows.
• Experience in high‑stakes decision‑support environments such as energy, power, utilities, commodities, trading, market operations, industrial systems, or financial workflows.

Compensation at Accenture varies depending on factors including location, role, skill set, and level of experience. The posting will remain open until 06/30/2026.

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.

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