Job description

Job Description We are building an AI Engineering function to enable productivity and agentic capabilities across the firm, for end users, developers, and business teams. As a Senior AI Platform Engineer, you will design and own the shared platform that powers AI systems firm-wide: inference services, agentic platforms, developer tooling, and observability. This is a financial services environment where data protection, auditability, and regulatory compliance are foundational requirements. You will ensure that AI capabilities are secure by default, auditable end-to-end, and easy for engineering teams to adopt. You will report to the Head of AI Engineering and partner closely with Security Engineering, AI Integration/Application teams, and core infrastructure groups. Responsibilities Platform Infrastructure Design, build, and operate the core AI platform, including managed LLM inference services (Amazon Bedrock and related), model access management, versioning, and routing across foundation models Design and operate shared integration layers, including MCP servers, an MCP registry/gateway, and authorization services that connect AI platforms with core firm systems Design and operate AI productivity data pipelines and dashboards for usage, cost, and adoption metrics Design the infrastructure that supports AI-assisted developer tooling (Linux VDI environments), office productivity integrations (M365/Excel), and autonomous agent frameworks Develop standardized inference and agentic AI platforms that teams can adopt across use cases, including reusable components for RAG, vector databases, and model integration patterns Security & Guardrails Partner with Security Engineering to embed security controls across the full AI lifecycle Design, with the AI Security Engineer and infrastructure/platform teams, the controls that prevent destructive agent actions: filesystem permissions, IAM policies, network allowlists, sandbox configurations, and execution-time policy enforcement Architect a default-deny posture: agents and tools access only explicitly permitted resources, with no ability to modify or delete production data unless specifically authorized through a human-approval workflow Implement pre-execution guardrails (hooks, policy engines) that intercept and validate agent actions before they run Ensure AI workloads operate within the corporate network boundary: VPC endpoints, PrivateLink, no public internet egress for inference traffic Enablement & Scale Build self-service onboarding so teams can consume AI platform services with appropriate access controls Design systems that enable cost-effective operation of AI workloads, including quota management and chargeback visibility Operate firm-wide AI applications and centrally managed AI services Define reference architectures and patterns that other engineering teams use to build on the platform Qualifications 10 years as an infrastructure, platform, or systems engineer, with demonstrated experience building and operating shared services consumed by multiple teams, on-premises and on AWS Strong expertise in AWS Bedrock (inference / agent core) and Azure OpenAI Strong expertise in designing and implementing MCP registries, gateways, servers and Authorization flows Hands-on experience supporting LLM-based workloads in production environments Experience designing and enforcing AI security controls at the platform layer in a regulated or security-sensitive environment Track record of building production-quality agentic AI patterns: tool use, function calling, MCP gateway/servers, retrieval-augmented generation, human-in-the-loop workflows Track record of building production-quality platforms and developer-facing services, with emphasis on usability, standardization, and reliability Strong written and verbal communication skills, with the ability to work effectively across security, application, and infrastructure teams Preferred Qualifications Experience in financial services, healthcare, or another heavily regulated industry Experience with Microsoft M365 Copilot / Copilot Agents Experience building observability pipelines (Splunk, ELK, Datadog, or Grafana) Familiarity with containerized and Kubernetes-based environments Experience with model fine-tuning workflows and ML lifecycle tooling Familiarity with DLP tooling and data classification frameworks The base salary range for this position is $200,000 - $325,000 per year. Arrowstreet Capital operates a robust talent acquisition program, and we also seek to compensate and reward our employees competitively within our industry and in line with our merit-based culture. Our approach to total compensation includes base salaries and annual discretionary bonuses, as well as a robust benefits package. The determination of a successful candidate’s base salary placement within the listed range will vary based on the candidate’s relevant experience and qualifications (which may also include relevant certifications, credentials and other education), the job responsibilities and scope, the commensurate resulting level of the position and other relevant factors. The listed range is also an estimate, and additional information regarding base salary and other elements of total compensation offered by Arrowstreet Capital to successful applicants will be communicated during the recruitment process. Arrowstreet Capital is a Boston-based systematic investment firm that manages global equity portfolios for institutional investors around the world. All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, ancestry, genetic information, age, pregnancy, medical condition, disability, veteran or military status, marital status or any other characteristic protected by federal, state, or local law. Arrowstreet Capital is committed to working with and providing reasonable accommodations for qualified individuals with disabilities and disabled veterans. If you need a reasonable accommodation for any part of the employment process due to a disability, contact us to discuss the nature of your request and contact information.

Requirements

Must-haves

We are building an AI Engineering function to enable productivity and agentic capabilities across the firm, for end users, developers, and business teams.
As a Senior AI Platform Engineer, you will design and own the shared platform that powers AI systems firm-wide: inference services, agentic platforms, developer tooling, and observability.
You will report to the Head of AI Engineering and partner closely with Security Engineering, AI Integration/Application teams, and core infrastructure groups.
Platform Infrastructure
Design, build, and operate the core AI platform, including managed LLM inference services (Amazon Bedrock and related), model access management, versioning, and routing across foundation models
Design and operate shared integration layers, including MCP servers, an MCP registry/gateway, and authorization services that connect AI platforms with core firm systems
Design and operate AI productivity data pipelines and dashboards for usage, cost, and adoption metrics
Design the infrastructure that supports AI-assisted developer tooling (Linux VDI environments), office productivity integrations (M365/Excel), and autonomous agent frameworks
Develop standardized inference and agentic AI platforms that teams can adopt across use cases, including reusable components for RAG, vector databases, and model integration patterns
Security & Guardrails
Partner with Security Engineering to embed security controls across the full AI lifecycle
Architect a default-deny posture: agents and tools access only explicitly permitted resources, with no ability to modify or delete production data unless specifically authorized through a human-approval workflow
Implement pre-execution guardrails (hooks, policy engines) that intercept and validate agent actions before they run
Ensure AI workloads operate within the corporate network boundary: VPC endpoints, PrivateLink, no public internet egress for inference traffic
Enablement & Scale
Build self-service onboarding so teams can consume AI platform services with appropriate access controls
Design systems that enable cost-effective operation of AI workloads, including quota management and chargeback visibility

Nice-to-haves

Experience in financial services, healthcare, or another heavily regulated industry
Experience with Microsoft M365 Copilot / Copilot Agents
Experience building observability pipelines (Splunk, ELK, Datadog, or Grafana)
Familiarity with containerized and Kubernetes-based environments
Experience with model fine-tuning workflows and ML lifecycle tooling
Familiarity with DLP tooling and data classification frameworks
Arrowstreet Capital is a Boston-based systematic investment firm that manages global equity portfolios for institutional investors around the world.

Application prep

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Keywords to mirror

aillmdesign systemsawsazurekubernetesml

Platform: workday
Effort: long