Trading, Investment & Optimization - QuantAI Engineer (Hybrid)

Accenture•Los Angeles, CA

2d•$59,100 - $188,100•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. 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. QuantAI 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.

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.

Nice To Haves

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.

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.
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.
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.