Job Description
Role & responsibilities:
Design, develop, and deploy end-to-end GenAI use cases and multi-step agentic workflows spanning the HR hire-to-retire lifecycle (recruiting/TA, onboarding, core HR, payroll & time, performance management, L&D, compensation & benefits, career/succession planning, oboarding & retirement)
- Build full stack applications (UI, APIs, backend services) that operationalize AI models and agents into tools usable by HR teams, managers, and employees
- Architect agentic workflows using orchestration frameworks that plan, reason, and execute multi-step tasks autonomously, with human-in-the-loop checkpoints where required
- Integrate LLMs with core HRIS/HCM systems(Workday) and case-management platforms via APIs and middleware
- Build and maintain Retrieval-Augmented Generation (RAG) pipelines and vector search to ground AI responses in HR policy, benefits, and compliance content
- Partner with enterprise IT, architecture, security, and data governance teams on integration design, security review, and infrastructure provisioning
- Work with HR business stakeholders to identify, prioritize, and scope AI/automation opportunities
- Stand up CI/CD, monitoring, and observability for deployed AI agents and applications
- Apply responsible AI practices bias testing, explainability, guardrails, human oversight and stay aligned with HR/employment AI compliance requirements
- Produce technical documentation and architecture diagrams; support production issues and iterate post-launch Technical Skills
- Languages: Python (primary), JavaScript/TypeScript, SQL
- AI / Agentic Frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, or AutoGen (proficiency in at least two)
- LLM Platforms: OpenAI, Anthropic Claude, Azure OpenAI Service, Google Vertex AI
- RAG / Vector Search: Pinecone, Weaviate, Chroma, Azure AI Search, or pgvector
- Full Stack Development: React/Next.js or Angular (frontend); Node.js or Python (FastAPI/Django) backend; REST and GraphQL API design
- Cloud & Infrastructure: Azure (preferred, given typical enterprise HR stack), AWS, or GCP; Docker; Kubernetes
- Automation & Low-Code Platforms: Microsoft Power Platform (Power Automate, Power Apps, Copilot Studio); UiPath or Automation Anywhere (RPA + AI)
- Integration / Middleware: MuleSoft, Boomi, or Azure Logic Apps for connecting AI services to enterprise systems
- DevOps: CI/CD (GitHub Actions, Azure DevOps, or Jenkins); Git-based version control
- Databases: PostgreSQL/SQL Server/MySQL; NoSQL (MongoDB, Cosmos DB)
- Security: OAuth2/OIDC, SAML SSO, secure API design, secrets management
Preferred candidate profile
- Hands-on experience with Microsoft Copilot Studio or a comparable enterprise agentbuilding platform
- Experience with prompt engineering, LLM evaluation, and testing/guardrail frameworks
- Exposure to MLOps/LLMOps practices for monitoring model and agent performance in production
- Relevant certifications (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning Specialty)
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