AI Engineer, Platform and Runtime

At HedgeServ were redefining whats possible in fund administration. With more than $700 billion in assets under administration we partner with the worlds 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.

Were 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. Weve 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 2000 experts across the globe with offices in the United States Grand Cayman Ireland Poland Bulgaria Luxembourg the Philippines and Australia. Weve 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 HedgeServs 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 HedgeServs 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.