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

Accenture•Los Angeles, CA

2d•$87,400 - $253,000•Hybrid

About The Position

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 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.

Requirements

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, backend services or APIs, databases or data flows, deployment paths, and practical debugging across several layers of the stack.

Nice To Haves

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.
Evidence that you can operate as a hands-on builder-manager: review technical work rigorously, unblock implementation, coach or manage engineers, help hire strong builders, communicate with senior stakeholders, and maintain a high bar without becoming a passive layer above the work.
Experience with tools and platforms commonly used in this work, such as React, Next.js, TypeScript, Tailwind, Vite, Vercel, Python, FastAPI, Electron, Tauri, Docker, cloud services, Kubernetes, CI/CD systems, observability tooling, model-serving infrastructure, or evaluation frameworks.
Experience building AI or agentic systems, including large language model (LLM) application patterns, agent orchestration, retrieval, tool use, memory, evaluation harnesses, guardrails, or human-in-the-loop workflows.
Experience in energy, power, utilities, commodities, trading, market operations, industrial systems, financial workflows, or other high-stakes decision-support environments.

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.
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, 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, regression testing, and evaluation of artificial intelligence (AI) and agentic behavior.
Use agentic development tools such as Codex and Claude Code fluently to accelerate delivery, while keeping human judgment, code review, security, testing, and architecture decisions firmly in the loop.
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 build the full-stack side of the team by shaping hiring signals, coaching fullstack engineers, setting technical standards, and creating repeatable build patterns that make each prototype easier to productize than the last.