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Agentic AI Architect
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EXL Service
Jersey City, NJ (In Person)
Full-Time
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Job Description
Agentic AI Architect EXL Service
- 3.
Full Job Description Job Description:
Technical skills : GenAI & Agentic Frameworks- Semantic Kernel/ LangGraph (or similar orchestration frameworks); LLM integration (Azure OpenAI, OpenAI APIs, etc.); Prompt engineering, prompt lifecycle design Retrieval & RAG
- Azure AI Search (indexing, vector search, hybrid search); Embedding pipelines and retrieval optimization; RAG design, grounding strategies, context management Tool Access & Integration
- MCP (Model Context Protocol) architecture and tool design; API design (Fast
API / REST
/ microservices); Integration with enterprise systems and third-party APIs AI Safety & Governance- NVIDIA NeMo Guardrails;Microsoft Presidio (PII detection/masking); Guardrails for prompt injection, hallucination control Evaluation & ModelOps
- Azure AI Foundry (model hosting, versioning, monitoring); Evaluation frameworks (LLM-as-judge, test datasets); Prompt/version control, cost/latency monitoring DevOps & Observability
- CI/CD pipelines (Azure DevOps / GitHub Actions); Logging, monitoring, observability (App Insights, etc.
Responsibilities:
Role & Responsibilities Overview:
Architecture & Technical Leadership Define end-to-end architecture for agentic AI-enabled platform across data, AI, orchestration, and integration layers with some real hands-on experience doing POCs Design and govern agentic orchestration framework for multi-step production workflows Establish architecture patterns for- RAG and grounding, Vector search and retrieval, MCP tool access layer, prompt management and evaluation Have a deep understanding of Agentic coding and best practices of using Agentic coding for large scale implementations Familiarity in implementing A2A or similar frameworks in a large scale environment Platform & Integration Design Define integration architecture across
- Lakehouse, ODS, document systems, Underwriting systems and third-party APIs Design configurable, metadata-driven framework for multi-LOB onboarding Define API/microservices patterns (Python/.NET hybrid) AI & GenAI Enablement Define where and how to use
- GenAI vs deterministic logic, agentic workflows vs pipeline workflows Establish multimodal integration approach combining structured, unstructured, and external data Design prompt lifecycle, evaluation, and optimization strategy Governance, Safety & ModelOps Define AI safety and guardrails (PII, hallucination control, policy constraints) Establish ModelOps and PromptOps frameworks Ensure explainability, auditability, and traceability of AI outputs Program Leadership Lead technical execution across AI, data, and platform teams Guide engineers (AI, data, full-stack) and ensure alignment with architecture Drive technical decisions and stakeholder communication