Software Engineer, Senior(.Net, Angular 6-9Yrs)
Summary
Senior full-stack engineer responsible for designing and building production services using C#, Python, and Angular. The role focuses on platform engineering and AI governance, integrating LLM-backed capabilities using AWS services like Bedrock and SageMaker.
Senior Engineers are expected to design solutions, not just implement against a specification handed down from elsewhere. Seniority carries equal design responsibility regardless of location, including ownership of feature and component design, proposed approaches, and trade-off analysis.
The Senior Software Engineer will build production services using Python, C#, and related technologies, develop front-end capabilities using Angular, and contribute to AI Governance capabilities across explain, observe, and prevent areas as initiative needs evolve.
· Integrate LLM-backed capabilities using the Anthropic Claude API, Model Context Protocol, and related platform services
· Contribute to AI Governance capabilities across explain, observe, and prevent areas, moving between whichever capability the initiative needs at a given stage
· Own the design of features and components within the assigned area, proposing implementation approach, alternatives, and trade-offs
· Mentor Engineers, review their code and designs, and help raise overall engineering quality
Participate in effort estimation and challenge estimates, scope, dependencies, or technical direction where warranted
· Proficiency in Python, with hands-on experience or working proficiency in C#, and PostgreSQL
· Full-stack proficiency, including Angular
· Hands-on experience using an AI coding tool in daily development work, such as Claude Code, AWS Kiro, OpenAI Codex, or a comparable AI-assisted coding tool
· Working knowledge of AWS services, including Bedrock, SageMaker, Lambda, and EKS
· Demonstrable exposure to LangChain, LangGraph, or a comparable agentic framework, with evidence of having built with one
· Comfort designing a solution from a problem statement, not only implementing a fully specified design
· Baseline AI literacy, including understanding how AI works, where it can go wrong, where it is useful, and how to direct it effectively
· Ability to evaluate AI-generated outputs before relying on them and use AI-assisted tools responsibly within team standards and review processes
· Prior work on systems with explainability, observability, or guardrail requirements
· Experience designing and delivering LLM-backed products, APIs, or AI-enabled production services
· Experience guiding engineering trade-offs, reviewing technical designs, and mentoring engineers in full-stack product delivery