Software Engineer - AI Application Engineering & Cloud

Software Engineer AI Application Engineering & Cloud

Location: Remote USA (San Francisco, Arlington VA, Denver, Chicago, Boston, NYC, Houston, Miami, Los Angeles, Seattle, Dallas, Minneapolis, Atlanta, Birmingham MI, and Irvine CA)



Duration: 6 10 Months



Interview: In-person interview required at a designated client office

Note:

• Old LinkedIn with location - with photo
• Local candidat only
• Remote role with mandatory interview or laptop pick up at a designated client location.

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Job Summary

We are seeking a Software Engineer / Lead Engineer to build and deploy AI-enabled applications in partnership with AI Engineers and product teams. The primary focus of this role is to provide strong software engineering leadership and hands-on development expertise needed to move AI use cases from PoC and MVP to production .

The candidate will be responsible for designing scalable, secure, high-performance, and production-ready systems , making architecture and technology decisions, owning code quality, and guiding technical direction. The candidate should have experience introducing and applying software engineering best practices-including architecture and design patterns, scalability, performance, security, testing, maintainability, and code quality-to AI engineering teams and AI solutions.

This is an AI application engineering role, not a deep ML/model-development role . The candidate does not need experience building or training AI models but should have practical experience with AI application patterns such as RAG, agentic workflows, LLM/API integrations, evaluation, and observability , and should be comfortable partnering with AI Engineers to build production-ready solutions.

The ideal candidate will also have strong Azure and DevOps experience , including infrastructure, security, identity, networking, environment management, CI/CD, and deployment readiness. Experience working with enterprise systems and standards within UAIS, Optum, UHG , or similarly complex enterprise environments is highly desirable.

Required Skills & Experience

• Strong software engineering fundamentals with a hands-on builder mindset and demonstrated experience delivering production-grade applications.
• Experience working as a Lead Engineer, Senior Software Engineer, or similar technical leadership role , with responsibility for architecture decisions and technical direction.
• Proven experience introducing software engineering best practices to AI engineering teams and AI solutions , particularly around architecture/design patterns, scalability, high performance, security, and production readiness.
• Strong experience designing and developing scalable, high-performance, secure, and production-ready software solutions .
• Practical experience working on AI solutions alongside AI Engineers, with a solid understanding of common AI application architecture patterns.
• Experience with RAG, agentic workflows, LLM/API integrations, AI evaluation, and observability .
• Strong Microsoft Azure experience across infrastructure, platform services, security, identity, networking, and environment management .
• Strong DevOps experience, including CI/CD pipelines, automated builds, testing, deployments, release processes, and environment management .
• Experience integrating applications with enterprise systems and working within complex security, compliance, and enterprise architecture standards .
• Strong experience with open-source and cloud technologies such as Neo4j, Cosmos DB, PostgreSQL, MongoDB, Kafka, Containers, Kubernetes (K8s), FastAPI , and related application frameworks.
• Strong understanding of data platform choices and architectural patterns, including when to use relational, NoSQL, graph, vector, and event-driven technologies.
• Experience with APIs, services, data integrations, distributed/cloud-native applications, and production deployment practices.
• Proven ability to deliver solutions across multiple Optum/UHG projects and work effectively within enterprise standards and constraints.
• Proficiency with GitHub workflows, branching strategies, pull requests, code reviews, and repository management .
• Experience with modern AI productivity tools such as GitHub Copilot, Claude Code, or similar tools .
• Strong communication and collaboration skills, with the ability to work effectively with AI Engineers, software engineers, product teams, DevOps teams, platform teams, and enterprise stakeholders .