AI Engineer

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 turn strong quantitative and artificial intelligence (AI) work 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.

What you’d work on:

• 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.

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.

What good looks like:

Must-have:

• Bachelor’s degree in computer science, engineering, mathematics, physics, economics, or a related field. An associate degree is acceptable with at least 2 additional years of directly relevant 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.
• 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.

Nice-to-have:

• Experience with tool-using systems, retrieval, evaluation pipelines, agent orchestration, or MCP-style integrations.
• Experience building expert-facing interfaces, workflow products, or technical demos that had to stand up in front of real users.
• Experience packaging desktop applications or supporting Windows-heavy enterprise environments.
• 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.

Team and environment:

QuantAI sits between quantitative research, agentic engineering, and product delivery inside Accenture. The team is small, hands-on, and built for people who want visible ownership and the chance to build something lasting. The goal is not one-off demos or deckware. The goal is reusable assets clients can trust, buy, and scale. Different strengths can thrive here, but on a team this size everyone works across both engineering surfaces. We care more about demonstrated depth in one area plus real fluency in the other than about a shallow checklist match across everything. You should expect direct technical feedback, growing scope, and close collaboration with quants and practice leadership. 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.