Senior AI Platform Engineer (Boston)

Job DescriptionWe 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.ResponsibilitiesPlatform InfrastructureDesign, build, and operate the core AI platform, including managed LLM inference services (Amazon Bedrock and related), model access management, versioning, and routing across foundation modelsDesign and operate shared integration layers, including MCP servers, an MCP registry/gateway, and authorization services that connect AI platforms with core firm systemsDesign and operate AI productivity data pipelines and dashboards for usage, cost, and adoption metricsDesign the infrastructure that supports AI-assisted developer tooling (Linux VDI environments), office productivity integrations (M365/Excel), and autonomous agent frameworksDevelop standardized inference and agentic AI platforms that teams can adopt across use cases, including reusable components for RAG, vector databases, and model integration patternsSecurity & GuardrailsPartner with Security Engineering to embed security controls across the full AI lifecycleDesign, 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 enforcementArchitect 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 workflowImplement pre-execution guardrails (hooks, policy engines) that intercept and validate agent actions before they runEnsure AI workloads operate within the corporate network boundary: VPC endpoints, PrivateLink, no public internet egress for inference trafficEnablement & ScaleBuild self-service onboarding so teams can consume AI platform services with appropriate access controlsDesign systems that enable cost-effective operation of AI workloads, including quota management and chargeback visibilityOperate firm-wide AI applications and centrally managed AI servicesDefine reference architectures and patterns that other engineering teams use to build on the platformQualifications10+ years as an infrastructure, platform, or systems engineer, with demonstrated experience building and operating shared services consumed by multiple teams, on-premises and on AWSStrong expertise in AWS Bedrock (inference / agent core) and Azure OpenAIStrong expertise in designing and implementing MCP registries, gateways, servers and Authorization flowsHands-on experience supporting LLM-based workloads in production environmentsExperience designing and enforcing AI security controls at the platform layer in a regulated or security-sensitive environmentTrack record of building production-quality agentic AI patterns: tool use, function calling, MCP gateway/servers, retrieval-augmented generation, human-in-the-loop workflowsTrack record of building production-quality platforms and developer-facing services, with emphasis on usability, standardization, and reliabilityStrong written and verbal communication skills, with the ability to work effectively across security, application, and infrastructure teamsPreferred QualificationsExperience in financial services, healthcare, or another heavily regulated industryExperience with Microsoft M365 Copilot / Copilot AgentsExperience building observability pipelines (Splunk, ELK, Datadog, or Grafana)Familiarity with containerized and Kubernetes-based environmentsExperience with model fine-tuning workflows and ML lifecycle toolingFamiliarity with DLP tooling and data classification frameworksThe 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.SummaryLocation: BostonType: Full time