Software Engineer II – Enterprise AI Products

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 across multiple technology disciplines, serving as a credible technical resource for LOB partners and internal engineering teams.

• Proficiency in Python, with experience in additional languages such as JavaScript, Type Script, 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 Git Hub 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 No
  
  SQL 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.