Job responsibilities

• Design and implement components of scalable, reliable agentic AI platforms for enterprise workflows
• Build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration
• Implement retrieval and context-engineering patterns including embeddings, semantic search, grounding, summarization, and prompt/version management
• Engineer cloud-native services on AWS using containers, serverless compute, and event-driven messaging patterns
• Optimize latency, throughput, scalability, caching, context efficiency, and cost across large language model workloads
• Develop secure, reusable APIs and integrations that connect AI capabilities to enterprise platforms and workflows
• Implement evaluation, experimentation, regression testing, and observability signals to improve quality and agent behavior over time
• Partner with product, platform, and engineering teams to translate requirements into resilient, measurable deliverables
• Contribute to technical standards and code quality through design reviews, documentation, and peer code reviews
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness. 
• Applies 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. 

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Hands-on experience building and operating production large language model applications, including agentic patterns and tool integrations
• Strong software engineering fundamentals with ability to deliver cloud-native services using containers and serverless designs on AWS
• Advanced python programming skills with experience writing production quality code
• Experience with retrieval-augmented generation approaches, including embeddings and semantic search, and practical context engineering
• Proficiency building React based front end experience that integrates backend API and provides strong attention to reliability, security, and performance
• Experience establishing or contributing to evaluation, testing, and monitoring practices for AI system quality and reliability
• Ability to troubleshoot complex issues across distributed systems, including asynchronous workflows and event-driven architecture
• Strong collaboration skills with the ability to communicate technical decisions and trade-offs clearly to partners
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security. 
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices. 

 Preferred qualifications, capabilities, and skills

• Experience deploying and operating workloads on Kubernetes-based platforms and container orchestration patterns
• Experience with experimentation frameworks and automated regression testing for large language model quality
• Familiarity with large language model cost governance and performance optimization techniques (for example, caching and context efficiency)
• Experience implementing guardrail patterns that support safe, reliable AI behavior in production
• Experience building reusable platform components and reference implementations adopted by multiple teams