About This Role
This lead software engineering position focuses on the architecture and hands-on implementation of scalable large language model systems and agentic AI platforms for enterprise use cases. The role involves designing cloud-native solutions, establishing evaluation and observability standards, and driving technical decisions to improve reliability, cost, and developer velocity.
Responsibilities
- Lead the architecture and hands-on delivery of scalable, reliable agentic AI platforms for enterprise workflows
- Design and build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration
- Architect retrieval and context-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management
- Engineer cloud-native AI services on AWS using containers and serverless patterns, event-driven messaging, and distributed data stores
- Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls
- Build well-governed APIs and integrations that connect AI capabilities to enterprise platforms, tools, and business processes
- Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior
- Define engineering standards for reliability, security, and safe AI operation across the platform lifecycle
- Mentor senior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership
- Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables, while validating outputs through peer review, automated testing, and secure coding standards; contribute learnings and reusable patterns to improve broader team effectiveness
- Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Requirements
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Experience architecting and shipping production large language model applications, including agentic workflows and tool integration patterns
- Strong software engineering fundamentals with ability to deliver cloud-native services using containers and serverless designs on AWS
- Proficiency designing distributed systems with asynchronous workflows, durable messaging, and scalable data access patterns
- Experience building retrieval-augmented generation solutions (embeddings, semantic search, grounding) and managing prompt lifecycle/versioning
