Trading, Investment & Optimization - QuantAI Engineer; Hybrid

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent and innovation‑led company with approximately 791,000 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.

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.

This is a small‑team build environment with real route‑to‑market access in energy, commodities, financial, trading, and industrial decision systems. The work needs to stand up in front of business decision makers and operators, not just engineers.

Quant

AI is building cutting‑edge AI‑native decision‑system assets for energy, commodities, financial, trading, and industrial operations. We are looking for engineers who can take strong quantitative and artificial intelligence (AI) work and turn it into enterprise‑safe products: interfaces, packaged desktop applications, APIs, services, workflow systems, and demos that are credible enough for pilots and durable enough for scaled delivery.

Success here is not raw model novelty or polished demos in isolation. It is strong algorithms wrapped in workflow, governance, evaluation, and packaging. This role is engineer‑first and shipping‑first. The engineering covers two surfaces that both ship as product: conventional systems on one side, agent‑assisted systems on the other. You should be able to operate across both — though you will likely lead with strength in one.

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.

• Ship across both cloud‑hosted services and locally distributed desktop applications, including Electron‑based apps when the workflow or client environment calls for it.

• Build enterprise hardening into the productization layer, including authentication, role‑based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.

• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.

• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.

• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.

• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

• Work…