Software Engineer III (Agentic AI)

Agent Development: Design, build, test, and deploy production-grade AI agents using Google ADK and other agentic platforms such as the Anthropic Claude SDK and LangGraph. Write clean, maintainable, and efficient code for agent workflows, tool integrations, and multi-agent orchestration. Platform Integration: Build agents that integrate with enterprise systems through tools, APIs, and Model Context Protocol (MCP) servers. Implement custom tools, skills, and plugins that extend agent capabilities across UKG's HCM platform. Feature and Project Delivery: Own execution and delivery of agent-powered features and projects, working with product management and engineering partners to clarify requirements, manage priorities and timelines, and deliver high-quality results. Technical Design: Participate in design reviews with peers, architects, and stakeholders. Contribute to the design of complex agentic systems and applications, including multi-agent coordination patterns, task delegation workflows, and agent communication protocols, with attention to performance, scalability, reliability, and maintainability. Code Review and Engineering Practices: Review code developed by other engineers, provide constructive feedback, and help maintain a high bar for technical excellence, including clean coding practices, appropriate design patterns, unit testing, and observability built from the ground up. Service Health and Quality: Maintain the health and quality of agent-powered services by proactively identifying and addressing issues. Use service health indicators, agent performance metrics such as task success rates, latency, and cost, and telemetry to recommend improvements. Conduct root cause analysis for agent failures and help implement measures to prevent recurrence. Agent Operations: Support agent deployment, monitoring, and lifecycle management. Apply practices for agent versioning, cost monitoring, performance optimization, and iterative improvement. Build observability into agent workflows, including execution tracing, decision logging, and failure analysis. Testing and Evaluation: Build testable agents and agentic systems; define and automate tests using appropriate frameworks. Apply agent-specific testing strategies, including prompt validation, agent evaluation, multi-step workflow testing, tool invocation testing, tool validation, and adversarial scenario testing. DevOps Model: Take ownership across the software lifecycle—from collaborating with product management on requirements through design, development, testing, deployment, and maintenance of software in production. Documentation and Knowledge Sharing: Document new features, enhancements, fixes, agent behaviors, and operational procedures. Share knowledge with teammates and contribute to training materials and team practices. Mentorship and Collaboration: Collaborate effectively across engineering and product teams and provide technical guidance to less-experienced engineers on agent development, tool integrations, testing, observability, and agent safety practices. Innovation: Stay current with emerging agentic AI technologies and platforms—including Google ADK, Anthropic Claude SDK, LangGraph, AutoGen, and CrewAI—and evaluate or adopt new capabilities, integrations, and orchestration patterns where they improve productivity, quality, or customer value. Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 4+ years of professional software development experience, with hands-on experience building LLM-powered applications or agentic AI systems. Deep expertise in one or more programming languages such as Python, Java, C#, JavaScript, or TypeScript. Experience building or contributing to production applications using platforms such as Google ADK, Anthropic Claude SDK, LangChain, LangGraph, or similar agentic frameworks. Understanding of agent orchestration patterns, tool-use architectures, prompt and context engineering, and agent design patterns such as ReAct and multi-agent collaboration. Experience integrating software or agents with enterprise systems through APIs, tools, and protocols such as REST APIs, MCP, and function calling. Experience with software architecture and design patterns, including the ability to contribute to scalable, reliable agentic systems in a DevOps model. Experience delivering complex software features or projects and working effectively with product, architecture, and engineering stakeholders. Proficiency with cloud technologies such as GCP (including Vertex AI and Google ADK), Azure (including Azure OpenAI), or AWS (including Bedrock), and version control systems such as GitHub. Strong problem-solving skills, attention to detail, and commitment to delivering high-quality agent-powered solutions. Experience building telemetry and observability for software or agentic systems, including execution tracing, decision logging, cost monitoring, or success-rate tracking. Strong communication and interpersonal skills, with the ability to explain technical concepts, collaborate in a team environment, and incorporate feedback into design and implementation decisions. Experience with agent evaluation frameworks, prompt testing, tool validation, or adversarial testing. Familiarity with CI/CD pipelines, automated testing, and production observability tools. Knowledge of agile software development methodologies. Familiarity with developing accessible technologies. Dedicated to diversity and inclusion initiatives.