Software Engineer II -- Enterprise AI Products Value Stream

Travelers•Saint Paul, MN

About The Position

As a member of Enterprise AI & Emerging Tech, you'll be joining a strategic and collaborative team committed to advancing our technology, data, and analytics capabilities — including some of the most consequential AI work happening across the enterprise. As a Software Engineer II for the Enterprise AI Products Value Stream within the Enterprise AI & Emerging Tech organization, you will have the opportunity to be an impactful member of a team leading change from day one. Engineers in this value stream work across the full arc of AI product delivery — from long-horizon strategic initiatives tackling complex, ambiguous problems where the path to production has yet to be defined, to hands-on field engagements that embed directly with Lines of Business to rapidly integrate enterprise AI platforms and prototype solutions at speed. You will partner with LOB stakeholders, product owners, architects, and platform teams to translate business problems into AI-powered capabilities that deliver measurable outcomes across the enterprise. This role is for engineers who seek out hard problems, operate effectively across different delivery modes, and build with production quality in mind.

Requirements

Bachelor’s degree in computer science, related STEM field, or its equivalent in education and/or work experience.
4 additional years of software engineering experience.

Nice To Haves

Proficiency in Python, with experience in additional languages such as JavaScript, TypeScript, or Java welcomed — particularly in candidates with backgrounds in enterprise AI integration.
Hands-on experience with AWS and/or Azure cloud services.
Experience with containerization and orchestration tools, including Kubernetes.
Proficiency with RESTful APIs and microservices architecture.
Experience with CI/CD tooling such as Jenkins or GitHub Actions.
Experience designing and delivering agentic workflows, LLM integrations, or AI-powered automations.
Demonstrated ability to work in agile, collaborative development environments.
Strong communication skills — able to engage directly with non-technical business stakeholders and translate operational problems into technical solutions.
Demonstrated experience with AI and automation solutions.
Experience with Terraform for infrastructure as code.
Familiarity with MongoDB or comparable NoSQL data stores.
Familiarity with Temporal or comparable workflow orchestration tools.
Experience with LLM evaluation methods, including designing evaluation datasets, defining metrics, and measuring model performance in production.
Experience with prompt engineering or RAG architectures.
Exposure to MCP server configurations or multi-agent frameworks.
Experience with observability tooling or distributed tracing in production environments.
Familiarity with ML observability and drift detection in production environments.
Familiarity with zero-trust security principles or secure-by-design development practices.

Responsibilities

Design, build, and deliver AI-powered solutions across a range of engagement types — from sustained strategic initiatives requiring deep discovery and architecture work, to rapid field engagements focused on integrating LOB workflows with enterprise AI platforms and validating feasibility through working prototypes.
Leverage enterprise AI capabilities including large language models, agentic workflows, cloud computing services (AWS/Azure), and container orchestration (Kubernetes) to solve complex, real-world business problems with a high degree of accuracy and speed.
Contribute to application and solution design in partnership with architects, product owners, and LOB technical leads — translating ambiguous business problems into technically sound, implementable solutions with clear production intent from kickoff.
Own delivery end to end — from scoping and design through development, handoff, and knowledge transfer — maintaining high standards for code quality and solution design at every stage.
Actively engage with LOB stakeholders to understand not just the problem at hand, but adjacent challenges and emerging needs — surfacing field intelligence that informs the broader AI product roadmap and strategic investment decisions, and sharing those findings in a discoverable way across the value stream.
Provide technical guidance and coaching to junior engineers on the team, serving as an informal technical lead within your team — setting the bar for code quality, solution design, and delivery standards without carrying formal management responsibility
Demonstrate subject matter expertise subject matter expertise across multiple technology disciplines, serving as a credible technical resource for LOB partners and internal engineering teams.