Build and maintain service interfaces and integrations (REST APIs, authN/authZ, audit/logging patterns) required for agent-driven applications.
Build reusable agent components (prompting patterns, tool connectors, policy/guardrail hooks, logging/tracing) to accelerate delivery across teams.
Automate build, deployment, testing, and release processes within the SDLC toolchain (CI/CD, controls, runbooks, change management).
Drive team adoption of enterprise-authorized AI-assisted engineering practices, improving code quality and delivery speed while enforcing validation standards (secure coding, peer review, automated testing).
Mentor and guide engineers through design reviews, code reviews, and engineering best practices; contribute to continuous improvement.
Monitor platform and agent service health (latency, reliability, cost); identify and resolve performance and stability issues; eliminate recurring issues via root-cause fixes or automated remediation.
Required qualifications, capabilities, and skills
Formal training or certification in software engineering concepts and 5+ years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability for production systems.
Advanced proficiency in Python and/or Java (strong preference for both).
Working knowledge of React (and modern frontend fundamentals) to contribute to or lead delivery of full-stack features.
Demonstrated end-to-end (“full stack”) engineering across services/APIs, integrations, CI/CD automation, and production operations.
Practical, hands-on AWS experience building cloud-native services (compute, storage, networking, IAM, observability).
Proficient across all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies including CI/CD, application resiliency, and security-by-design.
Demonstrated experience leading effective use of approved AI-assisted software development tools (coding, code review, test acceleration, troubleshooting), including setting validation expectations for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows (data sensitivity, secure handling of inputs/outputs, resiliency and security expectations); experience coaching engineers on safe, compliant adoption.
Experience mentoring engineers and leading code/design reviews in an enterprise environment (e.g., financial services).
Preferred Qualifications, Capabilities, and Skills
Experience in Agentic AI / building AI agents using existing models.
Experience with MCP development and tool-integrated agent architectures.
Experience with cloud-native design and delivery on AWS.
Strong hands-on AWS experience across compute, eventing/messaging, and observability.
Strong hands-on Java and Python experience.
Adobe Experience Manager (AEM) knowledge (author/publish, components/templates, integrations/APIs).
Experience with CI/CD and production operations (monitoring, incident support, root-cause analysis) in enterprise environments.
