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).
Preferred qualifications, capabilities, and skills
- Experience working with relational databases such as DB2 or PostgreSQL.
- 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.
- Financial services knowledge, with an understanding of the commercial lending space.
- Experience working in regulated environments requiring strong risk management, controls, and auditability.
