Java Architect
Hexaware
17 days ago
Full-time
On-site
United States
Description
JD 1. Agentic SDLC Design — Architect and implement end-to-end AI-augmented SDLC pipelines using autonomous agents and sub-agents that handle tasks spanning requirements analysis, code generation, review, testing, and deployment. 2. GitHub Copilot Agent Engineering — Design, configure, and extend GitHub Copilot agents, custom skills, and agent instructions to automate repetitive engineering tasks, enforce coding standards, and accelerate developer productivity across teams. 3. Multi-Agent Orchestration — Build and manage multi-agent systems with clearly defined agent roles, skills, and handoff protocols — including planner agents, coder agents, reviewer agents, and test agents — ensuring reliable and deterministic outcomes. 4. AI-Driven Code Quality & Governance — Integrate AI agents with static analysis, linting, and security scanning tools (e.g., SonarQube, CodeRabbit) to enforce quality gates, detect vulnerabilities, and provide real-time feedback within CI/CD pipelines. 5. Automated Testing with AI Agents — Leverage AI agents to auto-generate unit tests, integration tests, BDD scenarios (Gherkin/Cucumber), and test data — continuously improving test coverage with minimal manual effort. 6. Prompt Engineering & Skill Development — Design reusable, parameterised prompts and agent skills
JD 1. Agentic SDLC Design — Architect and implement end-to-end AI-augmented SDLC pipelines using autonomous agents and sub-agents that handle tasks spanning requirements analysis, code generation, review, testing, and deployment. 2. GitHub Copilot Agent Engineering — Design, configure, and extend GitHub Copilot agents, custom skills, and agent instructions to automate repetitive engineering tasks, enforce coding standards, and accelerate developer productivity across teams. 3. Multi-Agent Orchestration — Build and manage multi-agent systems with clearly defined agent roles, skills, and handoff protocols — including planner agents, coder agents, reviewer agents, and test agents — ensuring reliable and deterministic outcomes. 4. AI-Driven Code Quality & Governance — Integrate AI agents with static analysis, linting, and security scanning tools (e.g., SonarQube, CodeRabbit) to enforce quality gates, detect vulnerabilities, and provide real-time feedback within CI/CD pipelines. 5. Automated Testing with AI Agents — Leverage AI agents to auto-generate unit tests, integration tests, BDD scenarios (Gherkin/Cucumber), and test data — continuously improving test coverage with minimal manual effort. 6. Prompt Engineering & Skill Development — Design reusable, parameterised prompts and agent skills