VP, Principal AI Engineer

Acadian Asset Management LLC•Boston, MA

6h•$225,000 - $275,000•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in computer science, engineering, mathematics, or a related technical field, or equivalent practical experience.
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, and lifecycle management.
Demonstrated ability to work effectively across highly skilled, opinionated teams, synthesizing diverse perspectives into aligned technical outcomes.
Demonstrated ability to lead through influence, collaboration, and technical credibility rather than formal authority.
Hands-on experience with cloud platforms and modern AI tooling ecosystems. Amazon Bedrock and Sagemaker experience is preferred.
Experience partnering with Risk, Compliance, Security, or Legal teams to embed controls into technical systems.
Familiarity with quantitative, research-driven, or investment-oriented AI use cases.
Experience working in regulated or highly risk-aware environments, such as financial services or asset management.

Nice To Haves

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

Responsibilities

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.