Trading, Investment & Optimization - QuantAI Engineer (Hybrid)

About the role

The role involves building cutting-edge AI-native decision-system assets for energy, commodities, financial, trading, and industrial operations. The focus is on taking strong quantitative and AI work and turning it into enterprise-safe products, ensuring that the systems are credible for pilots and scalable for delivery.

Responsibilities

• 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 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.

Platforms and interfaces

• Own data flows, APIs, services, model-serving surfaces, front-end and desktop application surfaces, continuous integration and continuous delivery (CI/CD), and demo hardening.
• Build the systems that make quantitative work feel polished, reliable, and enterprise-ready for expert users and client stakeholders.

Agent-assisted systems

• Own the agentic harness layer — evaluation frameworks, reviewer loops, control-plane behavior, orchestration, and tool integration — that applications and MCPs wrap around.
• Design opinionated harnesses that expose through MCP or similar integration patterns without overfitting to one vendor or one moment in the tooling market.

Requirements

• Bachelor's degree in computer science, engineering, mathematics, physics, economics, or a related field. An associate degree is acceptable with a minimum of 2 additional years of experience and clear evidence of shipped engineering work.
• Minimum 3 years of experience in consulting or other client-facing technical delivery roles, with evidence that you have helped move products, internal tools, or workflow systems beyond proof-of-concept stage.
• Minimum 3 years of hands-on experience in one or more of the following areas: backend services, APIs and integrations, full-stack delivery, data pipelines, model-serving or machine learning workflows, or agentic orchestration systems.

Qualifications

• Strong coding ability in Python plus one complementary engineering surface such as TypeScript or JavaScript, front-end delivery, cloud or platform engineering, or infrastructure automation.
• Sound engineering judgment around enterprise hardening and evaluation, including experience with several of the following: authentication, role-based access control (RBAC), observability, security, release discipline, regression testing, or experiment frameworks for AI, machine learning, or agentic workflows.
• Experience with tools and platforms commonly used in this work, such as Electron, FastAPI, Docker, cloud services, evaluation tooling, agent orchestration frameworks, or MCP-style integrations.
• Experience building expert-facing interfaces, workflow products, technical demos, packaged desktop applications, or Windows-heavy enterprise deployments.
• Exposure to forecasting, anomaly detection, optimization, time-series systems, or other decision-support workflows.
• Experience in energy, commodities, financial, trading, market operations, or industrial workflows.

Benefits

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.