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 energetic 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.

📌 Integration Architect (Noida)
🏢 Bharat IT Services
📍 Noida