REF92857P_2026224137 - AI/ML Engineer - 4 to 8 years - Pune/Vizag (WFO) at WNSGlobalServices144

We are seeking a highly skilled Agentic AI Engineer to build and deploy multi-agent, goal-driven automation for document-heavy logistics workflows. The role owns the end-to-end agent lifecycle: from email/document ingestion to orchestrated workflow execution, system integrations (TMS/BL platforms), and a robust human-in-the-loop (HITL) + audit layer required for regulated shipping documentation. This is not a “model-only” ML role. You will engineer production-grade agentic workflows where agents do the work, the orchestrator decides what runs, exceptions are routed correctly, and every action is traceable. Key Responsibilities 1) Agentic Workflow Orchestration (Core) • Design and implement multi-agent architectures (classification, extraction, validation, customer follow-up, drafting, amendments, release) under a unified orchestrator that routes tasks, handles retries, manages state, and enforces guardrails. • Build case/task management for shipment documentation workflows: SLA prioritization, escalation rules, exception categories, and queue-based operations (shadow → assist → auto). • Implement confidence-driven automation (auto-run vs escalate vs stop) and structured fallbacks when upstream data or system access is limited.2) Enterprise Integration (TMS / BL / eBL / Content Systems) • Build secure integrations to enterprise systems using REST/SOAP APIs where available; design pragmatic fallbacks (file-drop, staging UI, controlled automation) when direct APIs are constrained (e.g., Citrix-hosted systems). • Integrate with: • Outlook/email ingestion and communication loops (request missing info, reminders, threaded responses). • Digiview / content repositories for archiving and retrieval of instruction/amendment emails and supporting documents. • BL platforms / eBL networks as required by process design (draft → review → release). • Create robust integration patterns: idempotency, deduplication, rate limiting, secure service accounts, sandbox/testing modes.3) GenAI + RAG for SOP-grounded Reasoning • Implement LLM-powered capabilities for classification, extraction, SOP-grounded validations, and structured decision support using RAG (vector DB), prompt engineering, and context management. • Optimize token usage and response structure for cost-efficient, scalable throughput.4) Document Intelligence & Data Pipelines • Build document handling pipelines (OCR/PDF parsing, table extraction, field normalization) for SI/draft/amendment content, including multilingual and semi-structured formats. • Engineer data pipelines to support continuous improvement: training data capture, labeling workflows, replay harness, and error analysis.5) Human-in-the-Loop (HITL) Console, Audit & Controls • Build a HITL review/approval layer (draft BL review, exception resolution, amendment approvals) with role-based access controls and supervisor capabilities—treated as a peer system with its own logs and controls. • Implement a full audit trail: every automated/manual action logged with timestamp, actor, input evidence, decision path, and output artifacts. • Ensure compliance-ready traceability for shipping documentation processes.6) ML Ops / LLM Ops & Production Reliability • Deploy and operate the solution using containerization (Docker/Kubernetes), CI/CD pipelines, monitoring, alerting, and rollback strategies. • Monitor and optimize performance (latency, cost, failure modes), ensure safe degradation, and maintain high availability.