Principal Software Engineer

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Role Summary

The Principal Software Engineer is a senior individual-contributor and enterprise technical leader responsible for shaping architecture, driving hands-on execution, and elevating engineering practices across multiple teams. This role leads the design and delivery of secure, scalable, resilient and high-performing software, data and AI solutions while translating complex business needs into simple, extensible technical approaches.

The role combines deep expertise in modern application engineering, cloud platforms, data engineering and AI with broad organizational influence. The Principal Software Engineer partners with engineering, product, architecture, data science and business leaders; mentors engineers and technical leaders; and accelerates the responsible adoption of Generative AI, Agentic AI and intelligent automation.

Primary Responsibilities

AI-First Technical Leadership

Provide technical leadership for AI-powered products, copilots, agent-based systems and intelligent workflows, from architecture through production operation

Design practical solutions using Azure AI Foundry, GPT models, RAG architectures, vector search, knowledge graphs and agent orchestration frameworks where they create measurable value

Establish reusable engineering patterns for prompt design, model evaluation, grounding, observability, guardrails, security, privacy and Responsible AI

Champion the effective use of AI-assisted engineering workflows to improve software design, development, testing, documentation, operations and analytics

Evaluate emerging capabilities across Generative AI, multimodal AI, Agentic AI and LLMOps, and translate relevant advances into secure enterprise solutions

Architecture and Solution Design

Lead the architecture, design and evolution of complex, business-critical systems across application, cloud, data and AI domains

Decompose complex business and technical problems into clear, maintainable and extensible architectures with well-defined boundaries and interfaces

Design distributed, cloud-native solutions using microservices, event-driven patterns, RESTful APIs, containers, serverless services and platform capabilities

Assess system interdependencies, scalability, reliability, performance, security, data quality and long-term maintainability when making design decisions

Define technical strategies and reference architectures that align product roadmaps with enterprise standards and modernization priorities

Technical Execution and Innovation

Remain sufficiently hands-on to create prototypes, validate architectural choices, resolve critical technical challenges and guide implementation quality

Drive engineering solutions across NET/C#, React/TypeScript, Python and Scala, selecting technologies based on product and platform needs

Guide the design of data pipelines, ETL/ELT processes and analytical solutions using Azure Databricks, Apache Spark and modern lakehouse patterns

Lead performance engineering for high-volume systems, including database design, query optimization, throughput, latency and resource efficiency

Create reusable frameworks, accelerators and engineering assets, and contribute to patents, publications or other intellectual property where appropriate

Engineering Excellence and Operational Readiness

Drive adoption of CI/CD, automated testing, Infrastructure as Code, containerization, observability and secure development lifecycle practices

Ensure production solutions meet expectations for availability, resilience, recoverability, supportability, compliance and data governance

Guide MLOps and LLMOps practices for versioning, deployment, evaluation, monitoring and lifecycle management of models and AI applications

Use architecture reviews, technical quality measures and operational feedback to identify systemic risks and improve engineering outcomes

Promote root-cause analysis and durable corrective actions for complex production and platform issues

Mentorship and Organizational Influence

Mentor senior engineers, architects and emerging technical leaders through design reviews, pairing, coaching and technical forums

Influence engineering standards and roadmaps across multiple teams without relying on direct authority

Build alignment across product, engineering, architecture, data science, security and business stakeholders on key technical decisions

Communicate complex technical topics clearly to both technical and non-technical audiences, including trade-offs, risks and recommended decisions

Foster a culture of continuous learning, experimentation, inclusion, accountability and engineering excellence

Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications

Experience

Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial I