Supports the delivery of solution or infrastructure development services for AI/ML initiatives, applying strong technical capability and hands‑on engineering experience.
Locations (8)
New York, New York, United States
Atlanta, Georgia, United States
Boston, Massachusetts, United States
Charlotte, North Carolina, United States
Chicago, Illinois, United States
Grandview Heights, Ohio, United States
Show all 8 locations
Hoboken, New Jersey, United States
Philadelphia, Pennsylvania, United States
Compensation
The salary range for this job is: New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $134,700 to $202,200 Bay Area California offices – $140,400 to $210,700 All other offices locations in the US, including Sacramento – $112,300 to $185,400
Responsibilities
Design, develop, test, deploy, and support production‑grade AI/ML, generative AI, and intelligent automation solutions
Solve complex technical problems through coding, debugging, testing, troubleshooting, and structured design remediation
Translate business and user requirements into technical designs, APIs, workflows, and supportable implementation patterns
Build and integrate LLM, RAG, and agentic solution components into enterprise applications and platforms
Contribute to system design across service boundaries, orchestration layers, data flows, security controls, and external integrations
Requirements
Bachelor’s degree in Computer Science, Engineering, or related technical field
Build and ship features for an AI-enabled revenue cycle management platform, including full-stack development, event-driven services, integrations, and AI agent orchestration.
Locations (2)
Chicago, Illinois, United States
United States
Compensation
The target base compensation for this position is $130,000 to $160,000 USD per year in most US locations. Final offer amounts are determined by multiple factors including location, local market variances, and candidate experience and expertise, and may vary from the amounts listed above.
Responsibilities
Build and ship features across a full-stack RCM worklist platform, from the user interface through backend services, APIs, and the data model
Develop event-driven services and workflows that route, prioritize, and track work across people and AI agents
Help build the shared automation foundation: the queues, state, and handoff logic that let an AI agent pick up a work item, finish it, or hand it back to a person, along with audit history, approval gates, and access controls for agent actions
Build and maintain integrations that connect the platform with EverHealth products, our clearinghouse, and third-party systems
The EDI & Integrations Specialist owns and supports EDI data flows between the McLeod TMS platform and Ryan Transportation’s trading partners. This role leads partner implementations, troubleshoots production issues, and coordinates with trading partners.
Responsibilities
Own and support EDI data flows between the McLeod TMS platform and Ryan Transportation’s trading partners
Design, build, and maintain scalable automation frameworks for web applications, APIs, and backend services, integrating AI tools and CI/CD pipelines to improve QA efficiency.
Responsibilities
Design, build, and maintain scalable automation frameworks for web applications, APIs, and backend services.
Develop automated functional, integration, regression, API, and end-to-end testing using tools such as WebdriverIO, Playwright, and Pytest.
Integrate automated testing into CI/CD pipelines, improving test coverage, release quality, and deployment confidence.
Use AI tools to accelerate test creation, identify edge cases, analyze failures, debug automation, and reduce repetitive QA work.
Partner closely with Engineering and Product to identify quality risks, troubleshoot complex issues, and ensure quality throughout the development lifecycle.
Design and build secure AI infrastructure for production AI agents, develop control planes, APIs, and tooling, and collaborate with researchers to transition prototypes into production.
Responsibilities
Design and build secure AI infrastructure for production AI agents
Develop Agent Control Plane capabilities that manage, monitor, and secure agent execution
Build embedded controls and security mechanisms directly into AI agents
Develop reusable services, APIs, and developer tooling that support AI applications
Build and maintain integrations with modern Agent SDKs and Model Context Protocol (MCP)
Requirements
Nice to have: Agent Control Planes
Nice to have: embedded controls or policy enforcement within AI agents
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