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.
Architect and deliver cloud-native microservices and APIs (REST/streaming), ensuring scalability, resilience, and strong security controls.
Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).
Communicate project status clearly and manage priorities across multiple initiatives.
Collaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, opportunity, and inclusion.
Codes in Java, AWS ECS, EKS, and Postgres
Utilizes AI agents (CoPilot, Claude) to improve quality and delivery timelines
Required qualifications, capabilities, and skills
Formal training or certification in Software Engineering and 5+ years applied experience
Strong system design, application development, and operational stability skills in production environments.
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.
Influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration, evaluation/guardrails).
Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).
Expertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).
Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).
Familiarity with DevOps practices and tools for continuous integration and deployment.
Preferred qualifications, capabilities, and skills
Cloud certification in AWS, GCP, or Azure.
Working knowledge of Python (for AI/ML based implementations) a plus
Experience with multi region service deployments and zero downtime deployment
Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs
Track record delivering scalable, reliable, and secure products from concept to launch.
Advanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a plus.
