Principal AI Engineer | Boston, MA, USA | Hybrid

Acadian Asset Management is a global, systematic investment manager at the forefront of data-driven investing since 1986. Headquartered in Boston, with locations in Singapore, London, and Sydney, we manage over $170 billion on behalf of leading institutions worldwide—including pension funds, endowments, foundations, and sovereign wealth funds. We harness advanced technology, rich datasets, and multidisciplinary expertise to help clients navigate complex markets and uncover insights that may be overlooked by traditional approaches.

What sets Acadian apart is our people. We foster a collaborative, intellectually curious environment where ideas are tested, diverse perspectives are welcomed, and innovation thrives. We’re united by a shared purpose: delivering effective client outcomes and supporting one another in work that’s both challenging and rewarding. We offer a flexible hybrid work environment, strong benefits, and a casual but focused office culture—all designed to support the meaningful, collaborative work that defines Acadian.

Position Overview:

The Principal AI Engineer is a senior technical leader responsible for defining, designing, and delivering enterprise-grade AI platforms and solutions across the firm. This role owns the firm’s AI architecture end-to-end, spanning agentic systems, retrieval-augmented generation (RAG), model integration, evaluation, security, and operationalization.

Operating as a hands-on architect, the Principal AI Engineer partners closely with investment teams and leaders across engineering, data, security, risk, and the broader firm. This role aligns diverse requirements and perspectives into coherent, scalable, and governable AI solutions. The Principal AI Engineer sets technical standards, drives architectural consistency, and owns the firm-wide AI modernization and adoption roadmap, ensuring AI capabilities mature from experimentation to durable, production-grade systems.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do:

• Own and evolve the firm’s AI architecture across agentic systems, retrieval-augmented generation (RAG), model integration, and production deployment.

• Partner closely with investment, engineering, data, risk, security, and business teams to align diverse requirements into scalable and governable AI solutions.

• Define and maintain technical standards, architectural patterns, and best practices for enterprise AI.

• Lead the design and delivery of production-grade AI systems, working alongside teams rather than centralizing all development.

• Enable and accelerate AI development within investment and research teams while ensuring alignment with enterprise architecture, controls, and platforms.

• Make and communicate architectural tradeoffs balancing innovation, risk, cost, time-to-value, and team autonomy.

• Ensure AI solutions integrate cleanly with existing platforms, data sources, and workflows across the firm.

• Embed governance, security, and risk controls into AI systems in collaboration with Risk, Compliance, and Security.

• Establish approaches for model evaluation, monitoring, and lifecycle management appropriate for a regulated environment.

• Continuously reassess and evolve AI architecture in response to new capabilities, risks, and firm priorities.

• Influence and help shape shared AI platforms, tooling, and reusable components.

• Evaluate emerging AI technologies, frameworks, and vendors with a practical, risk-aware lens.

• Drive adoption by ensuring AI solutions deliver measurable value to teams.

• Serve as a senior technical mentor, leading through technical credibility, collaboration, and influence rather than formal authority.

We’re Looking for Teammates With:

• Bachelor’s degree in computer science, engineering, mathematics, or a related technical field, or equivalent practical experience.

• Advanced degree or formal training in AI, machine learning, data science, or a related field is a plus, but not required.

• Significant experience in senior or principal-level software engineering roles, with demonstrated ownership of complex, production systems.

• Proven experience designing, building, and operating enterprise-grade AI or machine learning systems beyond prototypes and proofs of concept.

• Deep understanding of modern AI architectures, including agentic systems, retrieval-augmented generation (RAG), MCP, and large language model integration.

• Ability to reason clearly about tradeoffs involving risk, cost, scalability, and time-to-value, and to communicate those decisions effectively to technical and non-technical stakeholders.

• Strong software engineering fundamentals, including API design, distributed systems, and cloud-native architectures.

• Experience operationalizing AI systems, including testing, monitoring, evaluation