Engineer, Enterprise AI

Navitus Health Solutions, LLC

1d•Remote

About The Position

Due to growth, we are adding a Engineer, Enterprise AI to our team! The Engineer, Enterprise AI is responsible for designing, building, and operationalizing enterprise AI capabilities that enable the scalable adoption of Artificial Intelligence and Agentic AI across the organization. Working closely with the Enterprise AI Architect, this role translates enterprise AI architecture, patterns, and governance standards into implementable solutions and reusable technical components while partnering with Enterprise Architecture, Platform Engineering, Data Engineering, and Product teams to develop AI services, integrations, and operational frameworks that support generative AI, machine learning, and agentic workflows. The Engineer ensure AI solutions are implemented in alignment with enterprise architecture standards, security requirements, and regulatory compliance while enabling teams across the organization to safely and effectively leverage AI technologies to deliver business value. Is this you? Find out more below!

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems, or a related technical field, or equivalent work experience, required.
6-8 years of experience in software development, platform engineering, or machine learning engineering, including a minimum of 2-4 years of hands-on experience developing, integrating, or operationalizing Artificial Intelligence and Machine Learning solutions in production environments, required.
Experience building or supporting AI-enabled applications, data pipelines, model integrations, or AI platform components within cloud or distributed systems environments required.
Experience implementing AI integration patterns that connect AI capabilities with enterprise applications, APIs, data platforms, and cloud services required.
Experience developing or supporting generative AI and agentic AI workflows, including orchestration frameworks, tool integrations, context management, Model Context Protocol (MCP) servers or similar context services, and human-in-the-loop patterns to support governed AI decision-making, required.
Experience implementing or supporting AI operational practices (MLOps/LLMOps) including model deployment pipelines, monitoring, observability, lifecycle management, and operational controls, required.
Experience working within enterprise architecture standards and development frameworks, ensuring solutions aligned with architectural guardrails, security requirements, and platform standards required.
Experience supporting AI governance and responsible AI practices, including data privacy, model transparency, auditability, and compliance with enterprise and regulatory standards required.
Experience collaborating with architecture, engineering, data, and product teams to design and implement scalable AI-enabled solutions required.
Experience developing and supporting cloud-based services, APIs, and distributed systems that enable scalable AI capabilities and enterprise platform integrations required.
Strong development experience in modern programming languages, including Python, C#, or similar backend languages used for AI services and integrations required.
Proven ability to interview end-users for insight on functionality, interface, problems, and/or usability issues required.
Participate in, adhere to, and support compliance program objectives.
The ability to consistently interact cooperatively and respectfully with other employees.

Nice To Haves

AWS Certified Solutions Architect – Associate, AWS Machine Learning – Specialty, or equivalent cloud-based AI/ML certification preferred.
MLOps, AI platform engineering, or machine learning engineering certification preferred.
Responsible AI, AI governance, or AI ethics training/certification preferred.
Knowledge of PBM systems, claims adjudication processes, and data exchange patterns between payers, providers, and pharmacies preferred.

Responsibilities

Design, build, and maintain enterprise AI solutions and services that enable the scalable adoption of generative AI, machine learning, and agentic AI capabilities across the organization.
Implement enterprise AI architecture patterns and technical standards defined by the Enterprise AI Architect, translating reference architectures and design patterns into production-ready solutions.
Develop and support AI integration patterns that connect AI capabilities with enterprise applications, APIs, data platforms, and cloud services.
Build and maintain AI-enabled services and components, including model interfaces, orchestration services, agent frameworks, and reusable AI tooling for enterprise teams.
Support the implementation of Agentic AI workflows, including orchestration, tool integration, context management, and human-in-the-loop capabilities.
Implement and maintain AI operational capabilities (MLOps/LLMOps) including model deployment pipelines, monitoring, observability, lifecycle management, and operational controls.
Ensure AI solutions align with enterprise AI governance, responsible AI standards, and regulatory requirements, including data privacy, auditability, and model transparency.
Support AI platform development and enablement, working with Platform Engineering and Data Engineering teams to deploy, scale, and maintain enterprise AI infrastructure and tooling.
Other duties as assigned.