Live opening · Posted 7 days ago
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Job responsibilities
Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Develops secure high-quality production code, and reviews and debugs code written by others
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.
Lead end-to-end delivery of full-stack features spanning ReactJS UI, Java/Python backend services, SQL persistence, and Kafka event streaming.
Drive spec-driven development by authoring, reviewing, and maintaining executable/validated specifications (APIs, events, data contracts) as the source of truth; ensure implementation and tests remain aligned to the spec.
Use AI-assisted development frameworks (e.g., OpenSpec, Spec-Kit, or similar) to accelerate requirements-to-spec, spec-to-implementation, and spec-to-test workflows while maintaining engineering rigor.
Design and implement scalable, resilient REST APIs, including clear interface contracts and backward compatibility strategies. Design and implement AWS-based components leveraging S3 and AWS Lambda where appropriate.
Build and operate containerized services using Docker and Kubernetes (deployments, services/ingress, configuration/secrets, health checks, autoscaling). Implement event-driven workflows using Kafka (topic strategy, consumer groups, retry/DLQ patterns, schema evolution).
Provision and manage environments using Terraform (repeatable infrastructure, safe change management). Build LLM-based implementations (e.g., RAG, tool/function calling) with appropriate evaluation, monitoring, and controls.
Establish SDLC standards: design reviews, code reviews, automated testing, CI/CD, documentation, and operational readiness. Mentor and coach engineers; provide technical direction and promote continuous improvement.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
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
Strong backend development experience with Python (e.g., FastAPI/Flask), including API and service development.
Strong frontend development experience with ReactJS (modern React patterns, TypeScript/JavaScript).
Hands-on experience with Docker and Kubernetes running production workloads. Experience with Kafka (producer/consumer patterns, error handling, schema evolution).
Strong SQL skills and relational data modeling experience; query optimization and indexing.
Hands-on AWS experience including S3 and AWS Lambda; cloud security fundamentals (e.g., IAM-based access patterns). Experience using Terraform for infrastructure-as-code.
Spec-driven development experience, including: Defining and maintaining machine-readable specs (e.g., OpenAPI for REST and/or AsyncAPI for event interfaces); Driving implementation and automated tests from specs (contract testing, validation, backward compatibility checks); Using AI-assisted spec-to-code / spec-to-test tooling (e.g., OpenSpec, Spec-Kit, or similar).
LLM application experience in production (prompt/versioning practices, grounding/RAG concepts, evaluation/monitoring basics, secure data handling).
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