Cloud & DevOps: AWS, Docker, Infrastructure as Code, CI/CD, structured logging, metrics, tracing, audit trails
Enterprise integrations: Oracle ORC (HRIS — requisitions, candidates, scorecards), PeopleSoft incl. CSS (staffing & comp), background-check vendor APIs (e.g., First Advantage — bidirectional), DocuSign e-signature, Microsoft Exchange / Outlook via Graph API, ServiceNow ITSM
Sourcing channel integrations: LinkedIn Recruiter System Connect (One-Click Export ingest), Indeed Retrieve Candidates API (GraphQL + OAuth), Naukri ATS webhook ingest
Key Responsibilities
Own production operations of the platform: monitoring, incident response, root-cause analysis, patching, upgrades, and SLA adherence (“keep the lights on”).
Evolve the platform on the delivered foundation: design, build, and ship new features across the React frontend and FastAPI backend.
Maintain and extend agentic AI workflows — agent nodes, prompts, guardrails, tool bindings, and human-in-the-loop approval gates — including evaluation and bias-governance of AI scoring.
Operate and extend the connector framework integrating Oracle ORC, PeopleSoft, BGC vendors, DocuSign, Exchange/O365, and ServiceNow.
Maintain sourcing-channel ingestion pipelines — LinkedIn RSC candidate export, Indeed Retrieve Candidates API streaming, and Naukri webhook receivers — feeding the Sourcing Agent's scoring and shortlisting.
Manage data models and pipelines in MongoDB/DocumentDB, ensuring data quality, sync integrity across systems of record, and auditability.
Own the AWS infrastructure via IaC and CI/CD; manage environments, releases, security posture (SSO/RBAC), and cost.
Maintain observability: dashboards, agent audit trails, structured logs, alerts, and operational runbooks.
Partner with HR Ops, Recruiting, Legal/Compliance, and IT stakeholders to prioritize the backlog and translate business needs into platform capabilities.
Agent Operations: Monitor, tune, and deploy updates to specialized AI process agents (e.g., automated sourcing, offer generation, candidate triage) utilizing Jefferies' internal LLM gateways.
Ontology & Knowledge Base Maintenance: Continuously update the system’s Retrieval-Augmented Generation (RAG) knowledge graph (e.g., uploading new compliance policies, visa regulations, or compensation matrices) to ensure agents reason against the latest ground-truth data.
Data Sovereignty & PII Handling: Proven ability to architect data-masking pipelines to ensure highly sensitive data (Compensation, SSNs, Diversity metrics) is encrypted, scrubbed, and strictly isolated from broader LLM reasoning engines.
Algorithmic Bias & Regulatory Auditing: Familiarity with validating AI outputs against strict employment and compliance laws (e.g., EEOC guidelines, NY Fair Chance Act constraints) to ensure automated workflows remain legally defensible and auditable.
Lead knowledge transfer from the delivery partner and serve as the internal technical authority for the platform.
Required Skills & Experience Skill AreaWhat We Expect
- Full-stack engineering
- Strong professional software engineering experience, with ownership of production systems
- Python backend (FastAPI)
- Async Python services, REST APIs, Pydantic data modeling, SSE/streaming patterns
- React / TypeScript
- Modern React (hooks, router), TypeScript, Vite, Tailwind CSS; component-driven UI
- MongoDB / Amazon DocumentDB
- Document data modeling, aggregation pipelines, async drivers (Motor), performance tuning
- AWS cloud & DevOps
- ECS/EKS or similar container runtimes, IaC (Terraform/CloudFormation), CI/CD pipelines, Docker
- GenAI / agentic systems
- LLM application development — LangChain/LangGraph or similar, prompt design, human-in-the-loop workflows, evals
- Enterprise integrations
- Building/maintaining API integrations with enterprise systems (HRIS, ITSM, e-signature, email/Graph API); webhook ingest, OAuth, and partner-API patterns (e.g., LinkedIn RSC, Indeed, Naukri)
- Observability & operations
- Structured logging, metrics, tracing, dashboards, incident response, runbooks
