Senior Software Engineer – Enterprise AI Products

Responsibilities

• Design and lead the delivery of production‑grade AI‑powered solutions across a range of engagement types — from sustained strategic initiatives requiring deep discovery, architecture ownership, and long‑horizon planning, to rapid field engagements focused on integrating LOB workflows with enterprise AI platforms and validating feasibility through working prototypes.
• Drive technical decisions across multiple teams, establishing patterns and engineering standards that are adopted beyond the immediate team and serve as a reference for the broader organization.
• Lead stakeholder conversations with Lines of Business — translating complex technical capabilities into terms that communicate clear business value, and surfacing field intelligence that shapes strategic investment decisions and roadmap priorities.
• Apply production‑hardened machine learning experience to the design and evaluation of AI systems — including model selection, fine‑tuning, evaluation methodology, feature engineering, and the integration of traditional ML fundamentals alongside modern LLM‑based approaches.
• Own the technical quality of what the team ships — driving adherence to nonfunctional requirements including modularity, scalability, observability, recoverability, and testability across all delivered capabilities.
• Mentor engineers across the team and beyond, setting the learning example, defining best practices, and building engineering capability at an organizational level.
• Advocate for and advance engineering practices across the value stream — including source code management, CI/CD standards, test automation, and modern engineering principles — with an expectation of alignment across teams, not just your own.
• Identify opportunities where technology can drive business value, think proactively two to three quarters ahead, and contribute to the technical strategy of the AI Products value stream.
• Participate in and lead Engineering Chapter events and cross‑organizational technical forums, sharing knowledge in a discoverable and enterprise‑accessible way.
• Perform other responsibilities as assigned.

• 5+ years of software engineering experience with proficiency in Python; additional languages (JavaScript, Type Script, Java) a plus.
• Hands‑on experience with AWS and/or Azure, containerization and orchestration (Kubernetes), RESTful APIs, microservices architecture, and CI/CD tooling (Jenkins or Git Hub Actions).
• Production experience designing and delivering agentic workflows, LLM integrations, or AI‑powered automations; applied ML experience including model development, training, fine‑tuning, and evaluation.
• Demonstrated ability to lead architecture and solution design across teams; advanced delivery skills including design strategy, automated testing, feedback loops, and production health monitoring.
• Strong problem solver — uses data and proofs of concept to find creative solutions, measures impact, and optimizes; comfortable making decisions with broad implications and significant complexity.
• Strong communicator — able to translate complex technical capabilities into business terms, document initiatives clearly, lead stakeholder conversations, and foster inclusive, collaborative team environments.
• Advanced leadership skills — takes ownership in ambiguous situations, influences priorities, develops business partnerships, mentors others, and shares knowledge across the organization.
• Familiarity with Terraform, MongoDB or comparable No
  
  SQL, Temporal or comparable workflow orchestration, and MCP server configurations and multi‑agent frameworks.
• Experience with ML observability, drift detection, model performance monitoring, or distributed tracing in production environments.
• Familiarity with zero‑trust security principles or secure‑by‑design development practices in enterprise AI systems.
• Experience establishing or contributing to engineering standards at an organizational level.