Visa sponsorship will not be provided at this time.
Department OverviewThe 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 OverviewAs 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.
