• Lead the Central Operations Resilience and Engineering team, including engineers and program/governance leads • Build, develop, and retain a high-performing, multidisciplinary team • Serve as the primary escalation point for operational and technical issues that impact live operations • Apply disciplined engineering practices to incident response, root cause analysis, and corrective action development • Identify and lead continuous improvement initiatives that strengthen operational processes, tools, and outcomes • Own and manage a defined portfolio of operational engineering initiatives that support live operations • Partner closely with Site Operations, Design & Construction, Commissioning, Reliability Engineering, and regional teams

• Support our Shareholders by providing excellent service and quality results to our clients and the various branches of the federal government. • Engage in local markets, so community service is embedded in our process. • Nurture the strength of our workforce through mentorship and coaching, providing opportunities for growth and competitive benefits. • Encourage diversity, inclusion, and accountability at every level.

Role Description We are looking for a Senior Software Developer to join the team building AI Agent capabilities in Dayforce, with a focus on data-centric agents. This role is ideal for someone who can design, build, and ship production-quality agent systems—including a natural language to SQL (NL2SQL) solution—using modern agent frameworks (LangChain, LangGraph, Python) and AI development tools (Copilot, Claude, GitHub Copilot Agents, IDE agents, and internal agents). The goal is to accelerate delivery while maintaining high standards for quality, security, and operational excellence. This is a high-impact role. You will help transform how millions of users across 150+ countries interact with Dayforce by enabling natural language access to data and automating complex data workflows through intelligent agents. You will work across the full software lifecycle—architecture, design, prototyping, implementation, testing, release, and production operations—and partner closely with product, platform, and engineering teams. What you'll get to do - Design and Build Data Agents - Design, build, and maintain data-centric AI agents, including an NL2SQL solution that translates natural language queries into accurate and performant SQL against complex enterprise data models. - Develop agent architectures using LangChain, LangGraph, and Python, including tool use, multi-step workflows, and structured reasoning patterns. - Build robust evaluation, testing, and observability patterns to ensure correctness, reliability, and safety of agent outputs. - Design and implement guardrails, validation layers, and sandboxed execution environments to safely run generated SQL and agent-produced code in production. - Continuously improve agent accuracy and relevance through prompt engineering, fine-tuning strategies, and feedback loops. - Build and Ship High-Scale Systems - Design and implement new microservices, evolve existing services, and build or consume well-designed APIs to support agent and data workflows. - Own critical areas of the agent codebase and provide technical stewardship for shared patterns and libraries. - Improve performance, relevance, and reliability of data agent services through profiling, tuning, and iterative optimization. - Build tooling and automation around releases, integrations, and operational workflows. - Contribute to long-term improvements in production support, incident learnings, reliability, and operational maturity. - AI-Accelerated Engineering - AI-assisted development is a core and critical part of this role. You will actively use AI tools to increase engineering throughput while maintaining correctness, maintainability, and security. - AI-assisted scaffolding with rigorous human review. - AI-generated test suites and regression harnesses. - Fast refactoring and modernization supported by automated verification. - Agentic workflows for repetitive engineering tasks such as migrations, API clients, documentation, and runbooks. - Secure and responsible AI usage with strong review discipline, no sensitive data leakage, and traceable change sets. - Product Partnership and Engineering Culture - Partner with product and engineering stakeholders to understand data access needs and translate them into agent-driven solutions. - Translate business requirements into clear, well-reasoned technical designs. - Present system designs and complex technical topics to both technical and non-technical audiences. - Mentor peers through pairing, coaching, and high-quality code and design reviews. - Take an organization-first approach to accountability, prioritizing business outcomes over individual ownership. Qualifications - Strong Python development experience with a proven track record of building production systems. - Hands-on experience building agents using LangChain, LangGraph, or comparable frameworks, including tool use and multi-step workflows. - Experience building or contributing to NL2SQL or other natural-language-to-structured-query systems. - Strong SQL expertise, including complex query authoring, optimization, and schema comprehension across large, normalized data models. - Solid system fundamentals, including data structures, algorithms, design patterns, API design, and reliability engineering. - Experience with microservices and API-based architectures. - Experience with cloud platforms, with Azure strongly preferred. - Experience testing and evaluating LLM-based systems for accuracy, safety, and hallucination mitigation. - Familiarity with version control and CI/CD workflows using Git, GitHub, or Azure DevOps. Requirements - Demonstrated use of AI tools to accelerate feature delivery while maintaining high-quality pull requests. - Rapid prototyping and iteration supported by automated tests. - Refactoring and modernization with strong automated safety nets. - Effective use of AI for debugging, including log analysis, hypothesis generation, and targeted fixes. Nice-to-Have / Standout Experience - Experience with recursive language models (RLMs) and iterative inference strategies. - Experience with sandboxed execution environments for safely running dynamic or generated code. - Background in data engineering, including ETL/ELT pipelines, data modeling, and data warehousing. - Experience with retrieval-augmented generation (RAG), vector databases, and embedding-based retrieval. - Kubernetes experience, including deployments, scaling, and troubleshooting. - Azure PaaS experience, such as Azure SQL, Functions, Key Vault, Storage, Azure Monitor, and Log Analytics. - Kafka integrations across databases, microservices, or stream-processing systems. - Strong observability and reliability practices, including monitoring, alerting, and SLOs. - DevOps automation experience using tools such as Terraform, ARM templates, or PowerShell. - Experience with prompt engineering, fine-tuning, and LLM evaluation frameworks. Benefits - Excellent time away from work programs. - Comprehensive wellness initiatives. - Recognition through competitive pay and benefits. - Opportunities for personal and professional growth. - Commitment to community impact, including volunteer days and charity initiatives.

• Provide functional support for PeopleSoft Project Implementations • Translate functional requirements into technical specifications • Partner closely with PeopleSoft Business Analysts, System Analysts, System Integrators, Oracle, CAR, and external stakeholders • Assist with testing patches and fixes • Identify risks and obstacles within a project • Update manuals, job aids, SOPs, training materials, and other documents as needed • Deliver weekly status reports outlining work accomplished