Junior Agentic AI Engineer

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🔥 3 hours ago

🏈 Alabama, Arizona, +4 more states – Remote

🏈 Alabama – Remote 🌵 Arizona – Remote ⛰️ Colorado – Remote 🐊 Florida – Remote 🐎 Kentucky – Remote 🤠 Texas – Remote

⏰ Full Time

🟢 Junior

🤖 AI Engineer

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Dayforce

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

👥 HR Tech

☁️ SaaS

🏢 Enterprise

💰 $1G Post-IPO Debt - Dayforce on 2024-03

HR Tech • SaaS • Enterprise

Dayforce is a cloud-based human capital management (HCM) platform that delivers payroll, HR, workforce management, benefits, talent management, and workforce analytics as a unified, single-application SaaS solution for employers. It is designed for enterprise and mid-market organizations to manage payroll, time and attendance, scheduling, compliance, and employee lifecycle functions in one system.

📋 Description

• Build and deploy agentic systems for enterprise workflows — design and implement AI agents (and multi-agent systems) that reason and retrieve data across complex business processes and take action in enterprise systems. • Design and ship multi-step agentic systems — planner/executor, tool-using, multi-agent, and human-in-the-loop — for use cases including onboarding, underwriting, case review, and continuous monitoring. • Design orchestration, reasoning, and workflows — architect how agents plan, use tools, and coordinate across complex, multi-step processes. • Architect agent graphs in LangGraph (or comparable frameworks — CrewAI, AutoGen, Claude Agent SDK) with explicit state, durable execution, retries, and safe fallbacks. • Expose agents to production systems via well-typed tools and MCP servers; treat the tool surface area as a product. • Own full-stack implementation and integrations — build across LLMs, APIs, backend systems, and lightweight UIs to deliver complete, working solutions. • Build and own the retrieval layer powering our agents: chunking strategies, hybrid search (vector + keyword), reranking, and grounded citation. • Design and optimize embedding pipelines and vector indexes using pgvector and OpenSearch. • Develop agentic harnesses to accelerate development — create evaluation frameworks, toolchains, and workflows that enable rapid iteration and improve system reliability. • Own the eval stack: curate golden sets, maintain offline regression suites, implement LLM-as-judge, and run online A/B and shadow evals. • Ensure reliability, safety, and production readiness — implement guardrails, validation logic, and fallback mechanisms to ensure consistent and trustworthy behavior in production.

🎯 Requirements

• Bachelor's degree in Computer Science, Data Science, AI/ML, or related field, or equivalent practical experience through projects, research, internships, or professional work. • 1–5+ years in software engineering (full-stack or backend), or a strong recent graduate with demonstrable project or internship experience at equivalent depth. • Familiarity with LLMs or AI-based systems. • Internship or research experience in a production AI or data-intensive environment is a strong plus. • Proficiency in Python; comfortable with NumPy, Pandas, and Scikit-learn. • Hands-on experience with at least one LLM framework: LangGraph, LangChain, Claude Agent SDK, or OpenAI SDK. • Understanding of RAG architecture: embedding models, vector databases, hybrid search, and reranking. • Familiarity with prompt engineering best practices and awareness of LLM failure modes (hallucination, injection, drift). • Working knowledge of SQL and relational databases (PostgreSQL, MySQL, or similar). • Familiarity with Git version control and Agile/Scrum practices.

🏖️ Benefits

• Health insurance • Flexible work arrangements