Project Description
design, build, and operate AI orchestration pipelines that connect enterprise platforms — including Oracle ERP, ServiceNow (SNOW), and a broad ecosystem for all applications — into intelligent, automated workflows. enterprise integration, ensuring orchestrated agents and models deliver real business value across procurement, logistics, inventory, and fulfillment domains.
Architect and implement multi-agent and single-agent AI orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, or custom) to automate IT workflows end-to-end.
Design agentic pipelines with tool-use, memory, and reasoning loops that interface with Oracle SCM/ERP, ServiceNow, and third-party supply chain platforms.
Build and maintain prompt engineering strategies, chain-of-thought patterns, and retrieval-augmented generation (RAG) pipelines tuned for supply chain data and documents.
Evaluate and select orchestration tooling and LLM providers (OpenAI, Anthropic, Azure OpenAI, Google Vertex AI, open-source) based on use case fit, performance, and cost.
Enterprise Integration
Develop and maintain integrations between AI orchestration layers and enterprise systems including Oracle E-Business Suite / Oracle Cloud SCM, ServiceNow ITSM/ITOM, and WMS/TMS/MES platforms.
Design and implement API gateways, event-driven connectors, and middleware (REST, SOAP, GraphQL, gRPC, Kafka, MQ) to feed real-time data into orchestration workflows.
Collaborate with Oracle and ServiceNow platform teams to expose relevant APIs, webhooks, and data streams consumed by AI agents.
Ensure data consistency, idempotency, and error handling across heterogeneous system integrations.
Platform & Infrastructure
Deploy orchestration workloads on cloud-agnostic infrastructure (AWS, Azure, or GCP) using containerized services (Docker, Kubernetes) and serverless compute where appropriate.
Implement observability, logging, tracing, and alerting for AI pipelines using tools such as LangSmith, MLflow, Datadog, or OpenTelemetry.
Maintain security and compliance standards for AI systems handling supply chain data (PII, supplier data, inventory data).
Required Qualifications
Must-Have Experience
— designing and operating agentic AI systems, LLM pipelines, or intelligent automation workflows in production environments.
enterprise integration experience — building integrations with Oracle (EBS, Oracle Cloud, Fusion SCM) and/or ServiceNow via REST/SOAP APIs, webhooks, or middleware platforms.
Proficiency with AI/LLM orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, Haystack, or equivalent.
Strong programming skills in Python (primary) and/or Java/Node.js for building integration and orchestration components.
API design and consumption (REST, GraphQL, OpenAPI/Swagger) and message queuing systems (Kafka, RabbitMQ, Azure Service Bus, or similar).
Technical Skills
Cloud platforms: AWS, Azure, or GCP — platform agnostic, comfortable deploying on any major provider.
Containerization and orchestration: Docker, Kubernetes (EKS/AKS/GKE), Helm.
CI/CD and DevOps: Git, GitHub Actions, Azure DevOps, Jenkins, or equivalent.
Data integration: Familiarity with ETL/ELT patterns, data pipelines, and supply chain data models (PO, ASN, inventory, demand signals).
Monitoring and observability: Experience instrumenting AI workloads for production reliability.
Oracle Integration Cloud (OIC), Oracle API Gateway, or MuleSoft / Dell Boomi / Informatica for enterprise iPaaS.
Hands-on experience with ServiceNow Flow Designer, IntegrationHub, or Virtual Agent for AI-enhanced ITSM workflows.
vector databases (Pinecone, Weaviate, pgvector) and RAG architectures for enterprise document intelligence.
AI governance, responsible AI practices, and enterprise LLM security (prompt injection, data leakage prevention).