Build and refine agentic capabilities using the Smart SDK, including tool integration, orchestration patterns, and safety/reliability guardrails suitable for production use.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
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.
Write secure, high-quality production code and raise the bar through code reviews, debugging, and hands-on mentorship—improving maintainability, performance, and consistency across the codebase.
Drive operational excellence by identifying recurring issues and implementing automation, preventative controls, and reliability improvements to reduce toil and improve system stability.
Engineer data and search solutions using PostgreSQL (schema design, migrations, query tuning) and OpenSearch (indexing strategies, query relevance tuning) to support AI and analytics workflows.
Contribute to cloud-native engineering on AWS, partnering on infrastructure-as-code with Terraform and improving deployment safety, environment consistency, and observability.
Participate in technical evaluation sessions with internal partners and external vendors—assessing architecture, technical depth, and fit within existing platforms and information architecture.
Champion modern engineering practices and knowledge-sharing, contributing to communities of practice and accelerating adoption of leading-edge technologies.
Promote an inclusive team culture, contributing to diversity, opportunity, inclusion, and respect through daily collaboration and mentorship.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Strong hands-on expertise in Java/J2EE, Spring Boot, and microservices architecture, building secure, high-quality, production-grade systems.
Proficiency with AWS, Terraform, GitHub, Jenkins, and modern developer tooling (e.g., GitHub Copilot)
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Databases: proficiency with relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., DynamoDB, Redis, Cassandra), and GraphQL.
Containerization: experience with Docker and container orchestration (ECS, EKS, or Kubernetes).
Demonstrated experience developing, debugging, and maintaining software in a large corporate environment using one or more modern programming languages and database querying languages.
Demonstrable ability to write high-quality code in one or more languages, with strong debugging and troubleshooting skills.
Experience across the full Software Development Life Cycle (SDLC), from design and implementation through testing, deployment, and production support.
Demonstrated expertise with monitoring/observability tools (e.g., Splunk, Datadog, Dynatrace, CloudWatch) and proven capability to lead high-performing teams by influence—driving innovation, maintaining strong team health, and owning the end-to-end performance cycle
