•Design and develop full-stack software solutions using modern engineering approaches and patterns.
•Build and integrate AI-driven capabilities, including LLM-based services, orchestration, and workflow integrations.
•Develop and maintain cloud-native microservices and APIs (REST/streaming) with strong focus on scalability, resilience, and security controls.
•Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).
•Collaborate in a Scrum/Agile team, participate in ceremonies, and contribute to a culture of diversity, opportunity, and inclusion.
•Implement solutions primarily using Java, Spring Boot, and Python (AWS Lambda) , building microservices and Camunda workflow orchestration deployed on AWS ECS , backed by PostgreSQL .
•Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
•Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
•Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
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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.
•Strong application development skills with exposure to operational stability in production environments and hands-on experience in Java Full Stack Development.
•Experience with system design fundamentals, microservices patterns, and API development (RESTful and/or streaming).
•Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure)
•Proficiency with data technologies (relational and/or NoSQL) and common observability practices.
•Practical familiarity with LLMs / generative AI concepts and use cases (e.g., RAG, tool/prompt orchestration, guardrails/evaluation); working knowledge of Python for AI/ML integrations.
•Overall knowledge of the Software Development Life Cycle, and solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
•Familiarity with Docker, Kubernetes, Helm, modern CI/CD practices, multi-region service deployments, and zero-downtime release strategies.
•Strong communication skills, ownership mindset, proactive approach to continuous improvement, and a track record delivering scalable, reliable, and secure products from concept to launch.
•Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
•Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
