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

WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees.

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.

• 4–8+ years in software engineering / automation / AI engineering roles with demonstrated delivery of production systems.
• Proven experience with agentic AI orchestration frameworks (e.g., LangChain or similar orchestration frameworks) and building multi-step autonomous workflows.
• Strong experience integrating enterprise systems via APIs (REST/SOAP) and designing fallbacks for restricted environments.

Technical Skills

• Python proficiency and strong engineering fundamentals (design patterns, data structures, Git).
• Experience with vector databases and RAG patterns (Pinecone/Weaviate/Milvus or similar).
• Document processing expertise: OCR/PDF parsing, extraction pipelines, automation scripting.
• Cloud deployment experience on AWS/Azure/GCP and production-grade operational practices.
• Containerization & CI/CD: Docker, Kubernetes, pipelines, observability.

Production & Governance

• Hands-on experience implementing HITL workflows, audit logging, RBAC, and operational dashboards.
• Ability to build safe autonomous systems: confidence gating, policy constraints, replay testing.

Preferred / Good-to-Have (Strong Differentiators)

• Prior work in logistics/shipping documentation workflows (SI/BL/HBL, amendments, trade-lane SOPs).
• Experience with workflow/case management platforms or orchestration engines beyond agent frameworks.
• Experience with RPA/UI automation as fallback in constrained environments (Citrix-style).
• Familiarity with secure enterprise integration patterns: service accounts, secrets management, network controls.