Build and enhance high-performance backend services and APIs (REST/GraphQL), integrating with enterprise platforms, relational databases, search technologies, and distributed data systems.
Develop modern operational and administrative user interfaces using React and TypeScript, delivering intuitive experiences that improve platform usability, productivity, and supportability.
Design and implement event-driven and streaming architectures leveraging Kafka, including event modeling, schema governance, topic strategy, and resilient producer-consumer patterns to enable scalable and loosely coupled systems.
Champion engineering excellence through code reviews, troubleshooting, refactoring, performance tuning, and mentoring, while raising quality standards across the team.
Lead initiatives to improve application reliability, observability, and operational stability through automation, proactive monitoring, distributed tracing, incident management, root cause analysis, and continuous reduction of technical debt.
Leverage enterprise-approved AI-assisted engineering tools and platforms to accelerate software delivery, improve developer productivity, automate repetitive tasks, and enhance code quality while adhering to established governance, security, and validation practices.
Evaluate, design, and implement AI-powered and agentic solutions that optimize software development, operational workflows, system support, and engineering processes, ensuring measurable business and productivity outcomes.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
10+ years of hands-on software engineering experience building and supporting enterprise-scale applications.
Strong expertise designing and developing backend services using Java, Spring Boot, distributed systems, and microservice architectures.
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
Hands-on experience building and consuming APIs using REST and/or GraphQL.
Strong experience with relational databases, SQL optimization, and data access patterns.
Experience implementing event-driven and streaming architectures using Kafka or equivalent messaging platforms.
Experience building modern frontend applications using React and TypeScript.
Experience with observability and operational tooling such as Splunk, Grafana, Kibana, ELK, OpenTelemetry, Postman, or equivalent platforms.
Proficiency with modern engineering tooling including Git, GitHub, Maven, Jenkins, SonarQube, IntelliJ IDEA, VS Code, Jira, and Confluence.
Preferred qualifications, capabilities, and skills
Working knowledge of Python with experience using Python for automation, data processing, or AI-enabled solutions is highly desirable
Experience designing and deploying AI agents and agentic workflows for software engineering, operations, or business process automation.
Understanding of Model Context Protocol (MCP) and modern AI integration patterns.
Knowledge of prompt engineering techniques, AI workflow design, and human-in-the-loop systems.
Experience creating agentic automations that improve SDLC productivity, testing, code reviews, documentation generation, CI/CD operations, or incident management.
Demonstrated ability to establish governance, validation, and quality controls for AI-generated code and solutions.
Working knowledge of Elasticsearch/OpenSearch, including index design, query optimization, and operational management.
