Responsibilities and essential job functions include but are not limited to the following: Designs, builds, and operates AI-powered automation and agent-based systems across multiple phases of the software development lifecycle (SDLC). Designs and builds shared cloud-native platforms, services, and frameworks that enable other engineering teams to build, deploy, and operate AI agents and automation safely and efficiently. Provides paved-road infrastructure, APIs, templates, and reference implementations that accelerate adoption of agentic and GenAI capabilities across multiple teams. Implements agentic workflows to analyze requirements, generate test scenarios, identify risks, and surface quality gaps early in the development cycle. Develops AI-driven solutions for automated testing, performance testing, quality validation, and A/B experimentation. Builds and maintains scalable automation frameworks for data processing, model execution, validation, and deployment. Participates actively in design reviews, code reviews, and incident retrospectives to uphold engineering excellence. Evaluates emerging AI, GenAI, and automation tools through prototypes and proofs of concept, recommending scalable adoption strategies. Contributes to and promotes strong software engineering practices including testing, observability, documentation, and operational readiness. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, or protected veteran status, or any other characteristic protected by law. Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances. 7+ years of professional experience in software engineering or platform engineering roles. Strong hands-on experience building and operating production-grade software systems. Experience building and operating shared cloud infrastructure or internal platforms used by multiple engineering teams. Proficiency in at least one modern programming language such as Python, Java, Scala, C# or similar. Experience designing and working with distributed systems, service-oriented architectures, and APIs. Experience with CI/CD pipelines, automated testing, and modern DevOps practices. Working knowledge of cloud platforms such as AWS, Azure, or equivalent. Experience working with relational and NoSQL databases. Strong understanding of software engineering fundamentals including testing strategies, code reviews, and system design. Excellent communication and collaboration skills. Experience applying Generative AI or AI/ML techniques to software development, quality engineering, or automation workflows. Experience building agentic or GenAI-powered systems using frameworks and platforms such as LangChain, AWS Bedrock, or similar orchestration and model hosting technologies. Familiarity with agentic AI concepts including LLM orchestration, autonomous agents, retrieval-augmented generation (RAG), prompt engineering, and vector databases. Experience designing enablement platforms, SDKs, or reusable services that allow other teams to build and extend AI-driven or automation workflows independently. Experience defining guardrails, abstractions, and operational standards that enable teams to safely adopt AI and agent-based systems at scale. Experience working with open-source LLMs and AI frameworks. Hands-on experience with data platforms and streaming technologies such as Kafka, Spark, Hadoop, or Databricks. Knowledge of MLOps practices including model deployment, monitoring, governance, and lifecycle management. Experience building internal developer platforms, tooling, or productivity solutions. Ability to mentor other engineers through technical leadership and example, without direct people management responsibility.