Integration Architect

Senior AI Integration Architect

Job Summary: The Senior AI Integration Architect serves as the bridge between model capabilities and enterprise infrastructure. You will design agentic workflows, complex RAG architectures, and API middleware that connect fine-tuned SLMs directly to legacy software platforms (ERPs, CRMs, core banking systems).

Key Responsibilities

• Agentic System Engineering: Design multi-agent workflows (using LangChain, LlamaIndex, AutoGen, or Semantic Kernel) that execute tasks by invoking enterprise APIs.
• Advanced RAG & Vector Systems: Build enterprise Retrieval-Augmented Generation (RAG) pipelines with hybrid vector/text search, contextual re-ranking, and dynamic chunking.
• Systems & Middleware Integration: Build secure REST/gRPC connectors, webhooks, and middleware to bridge SLMs with legacy systems like SAP, Oracle, Salesforce, and core banking backends.
• Enterprise Security & Privacy: Implement role-based access control (RBAC), data loss prevention (DLP), and PII redactors to ensure client data remains fully secure.

Mandatory Skills (Must-Haves)

• Agentic Frameworks: Hands-on mastery of LangChain, LlamaIndex, or AutoGen for orchestrating complex tool-calling workflows.
• Vector Databases & RAG: Expert-level knowledge of vector databases (Qdrant, Pinecone, Milvus, or pgvector) and hybrid search strategies.
• Enterprise Integration Paradigms: Proficiency in API management (REST, GraphQL, gRPC), messaging queues (Apache Kafka, RabbitMQ), and microservices architecture.
• Secure Software Development: Proven ability to implement PII masking, tokenization, and enterprise identity standards (OAuth 2.0, SAML, RBAC).
• Backend Engineering: Advanced proficiency in Python or TypeScript/Node.js for backend service construction.