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

Job Description

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.

Qualifications

• 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.