Job description

At HedgeServ, we’re redefining what’s possible in fund administration. With more than $700 billion in assets under administration, we partner with the world’s most forward-thinking investment managers – across private equity, private credit, endowments, hedge funds and more – to deliver seamless, tech-enabled solutions that drive performance. Our proprietary platform, enhanced by machine learning and robotic process automation, gives clients real-time insights and unmatched control over their operations. Alongside our technology, we offer award-winning service through our team-based approach -- led by a deeply experienced team of industry experts. Our solutions span the full investment lifecycle, including fund accounting, middle office, risk, compliance, tax, and investor services. We’re a future-focused company, empowering our people through a robust career development framework, clear career trajectories with structured learning paths, training, and progression plans. We invest in leadership development and in our collaborative culture, creating space for talent to grow. Our corporate values – Relationships, Support, Innovation, and Expertise – create a sense of shared purpose and belonging, and we recognize our employees sit at the core of our success. We continue to innovate and evolve through our employees, working together to achieve our shared vision and mission. HedgeServ supports employees through a variety of offerings, including remote and hybrid working arrangements, and fully paid comprehensive health and well-being benefits. We’ve been recognized as an employer of choice, earning a top 100 workplaces designation. Founded in 2008, HedgeServ has grown into a global organization with over 2,000 experts across the globe, with offices in the United States, Grand Cayman, Ireland, Poland, Bulgaria, Luxembourg, the Philippines, and Australia. We’ve earned numerous accolades, including Top Overall Administrator, along with #1 rankings for providing alternative asset services in Accounting, Technology, Client Service, Investor Services, Alternative Fund Expertise, Reporting, and Regulatory Expertise. Visa sponsorship will not be provided at this time . Department Overview The AI Engineering team builds secure, reliable, and reusable AI capabilities for HedgeServ’s business and technology platforms. We partner across engineering, security, data, and operations to move solutions into production. We integrate AI into existing workflows with a focus on measurable outcomes, production discipline, responsible AI, security, human oversight, and evidence-based evaluation. Role Overview As a Senior AI Platform and Runtime Engineer, you will build and operate HedgeServ’s shared AWS platform for AI applications and agents. AWS is the primary platform, while experience applying comparable patterns on other enterprise clouds is valued. You will own runtime infrastructure, identity and security, model and tool access, deployment, and reliability, delivering scalable capabilities and reusable standards for engineering teams. Key Responsibilities Design, build, and operate secure, scalable AI and agent platforms on AWS, applying relevant patterns from other enterprise clouds. Own platform architecture across infrastructure, AI services, identity, networking, observability, and runtime operations. Automate infrastructure and delivery using Terraform, AWS CDK, and CI/CD tooling. Deploy and support production LLM and agent workloads using Amazon Bedrock and related AWS services. Build resilient distributed systems using asynchronous processing, durable state, retries, idempotency, and recovery. Own agent-runtime reliability, including scaling, performance, observability, service-level objectives, incident response, pause-and-resume recovery, and cost optimization. Implement identity and authorization controls, least privilege, encryption, secrets management, and auditable data and tool access. Govern model access, quotas, provider onboarding, evaluation, and lifecycle controls. Partner with engineering, architecture, security, product, and business teams to establish reusable standards and patterns. Required Qualifications 5 years of software engineering experience, including at least 1 year delivering LLM or agent-based applications in production. Demonstrated experience in cloud, platform, site reliability, or software engineering with responsibility for production systems. Strong enterprise-cloud experience across identity, networking, compute, storage, and event-driven architectures. Hands-on AWS experience is preferred; deep experience with another enterprise cloud and the ability to transfer those skills to AWS are also valued. Experience with infrastructure as code, containerized workloads, and automated delivery using Terraform, AWS CDK, Docker, ECS, EKS, or comparable cloud-native technologies. Hands-on experience building or operating production LLM, generative AI, or agent applications using model APIs, structured outputs, tool integration, retrieval, or comparable patterns. Experience designing or operating distributed systems with asynchronous processing, state management, retries, idempotency, resilience, and recovery. Working knowledge of observability, incident response, cost management, and enterprise security practices. Strong analytical, troubleshooting, communication, and collaboration skills, with the ability to work across technical and business teams. Preferred Experience 7 years of cloud, platform or software engineering, including substantial production AWS experience and responsibility for architecture. Strong knowledge of identity, networking, compute, storage and event-driven services. Experience building shared platforms, internal developer products, APIs, SDKs, or reusable services for multiple engineering teams. Experience leading architecture reviews, establishing technical standards, documenting platforms, or mentoring engineers. Experience with agentic AI platforms, multi-agent systems, Model Context Protocol, or similar tool and context integration patterns. Familiarity with OAuth 2.0, OpenID Connect, workload identity, Microsoft Entra ID, or comparable identity platforms. Experience with AI governance, model evaluation, safety testing, policy enforcement, or lifecycle management. Experience in financial services or another regulated environment with strong audit, privacy, security, and access-control requirements. Relevant cloud, security, platform, or AI certifications, or equivalent practical experience.

Requirements

Must-haves

Visa sponsorship will not be provided at this time .
Department Overview
We integrate AI into existing workflows with a focus on measurable outcomes, production discipline, responsible AI, security, human oversight, and evidence-based evaluation.
Design, build, and operate secure, scalable AI and agent platforms on AWS, applying relevant patterns from other enterprise clouds.
Own platform architecture across infrastructure, AI services, identity, networking, observability, and runtime operations.
Automate infrastructure and delivery using Terraform, AWS CDK, and CI/CD tooling.
Deploy and support production LLM and agent workloads using Amazon Bedrock and related AWS services.
Build resilient distributed systems using asynchronous processing, durable state, retries, idempotency, and recovery.
Own agent-runtime reliability, including scaling, performance, observability, service-level objectives, incident response, pause-and-resume recovery, and cost optimization.
Implement identity and authorization controls, least privilege, encryption, secrets management, and auditable data and tool access.
Govern model access, quotas, provider onboarding, evaluation, and lifecycle controls.
Partner with engineering, architecture, security, product, and business teams to establish reusable standards and patterns.
5 years of software engineering experience, including at least 1 year delivering LLM or agent-based applications in production.
Demonstrated experience in cloud, platform, site reliability, or software engineering with responsibility for production systems.
Experience with infrastructure as code, containerized workloads, and automated delivery using Terraform, AWS CDK, Docker, ECS, EKS, or comparable cloud-native technologies.
Hands-on experience building or operating production LLM, generative AI, or agent applications using model APIs, structured outputs, tool integration, retrieval, or comparable patterns.
Experience designing or operating distributed systems with asynchronous processing, state management, retries, idempotency, resilience, and recovery.

Nice-to-haves

Experience building shared platforms, internal developer products, APIs, SDKs, or reusable services for multiple engineering teams.
Experience leading architecture reviews, establishing technical standards, documenting platforms, or mentoring engineers.
Experience with agentic AI platforms, multi-agent systems, Model Context Protocol, or similar tool and context integration patterns.
Familiarity with OAuth 2.0, OpenID Connect, workload identity, Microsoft Entra ID, or comparable identity platforms.
Experience with AI governance, model evaluation, safety testing, policy enforcement, or lifecycle management.
Experience in financial services or another regulated environment with strong audit, privacy, security, and access-control requirements.
Relevant cloud, security, platform, or AI certifications, or equivalent practical experience.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiawsci/cdllmdocker

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Vision