Senior AI Engineer

Acadian Asset Management LLC•Boston, MA

27d•$175,000 - $225,000•Hybrid

About The Position

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 $232 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 Senior AI Engineer is a senior technical leader responsible for defining, designing, and delivering enterprise-grade AI platforms and solutions across the firm. This role aligns diverse requirements and perspectives into coherent, scalable, and governable AI solutions. The Senior AI Engineer sets technical standards, drives architectural consistency, and owns a slice of 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, physics, robotics, mathematics, or a related technical field, or equivalent practical experience.
5+ years of relevant experience with 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 LLMs.
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.
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.
Broad cross systems knowledge across AWS, K8s, software architecture, day 2 operations.

Nice To Haves

Advanced degree or formal training in GenAI and Agents, machine learning, data science, or a related field is a plus, but not required.
Amazon Bedrock is preferred.

Responsibilities

Owns a slice of the firm’s AI architecture end-to-end, spanning agentic systems, software factories, knowledgebases, loops, custom harnesses, context and memory engineering, A2A, agentic deep research, retrieval-augmented generation (RAG), LLM training, inference optimization, model integration, evaluations (evals).
Partner closely with investment, engineering, sales, data, risk, security, and business teams to align diverse requirements into scalable and governable AI solutions.
Lead the design and delivery of production-grade AI systems, working alongside teams.
Enable and accelerate AI development within investment and research teams while ensuring alignment with enterprise architecture, controls, and platforms.
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.
Drive adoption by ensuring AI solutions deliver measurable value to teams.
Serve as a technical mentor, leading through technical credibility, collaboration, and influence rather than formal authority.