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# AI Engineer

- **Company:** Cognizance Technologies
- **Location:** Silver Spring, MD, United States
- **Salary:** $135,000.0 - $145,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Architectural Patterns, Audit Trail, Databases, Data Auditing, Data Governance, Data Intelligence, Parsing, Software Architecture, Enterprise Data Management, Data Logging, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Generative AI
- **Published:** May 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5149fa060e4e04a1

## About the Role

Do you have experience in Validation design?

## Description

We are seeking an AI Engineer for to design and develop next-generation, agentic AI tools that revolutionize complex document review and data analysis workflows. In this role, you will build intelligent multi-agent systems that allow users to interrogate dense technical documents, execute multi-step analytical tasks, and automatically populate operational dashboards.

You will orchestrate advanced generative AI, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and automated evaluation systems to create highly reliable, secure, and self-correcting software architectures. You will collaborate closely with data standardization specialists and ontologists to integrate advanced data schemas and controlled terminologies into the AI pipeline., Multi-Agent Orchestration: Design and implement working agentic AI prototypes using frameworks like LangGraph, LangChain, or CrewAI capable of executing multi-step analytical and reasoning processes for document reviews
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Interactive Query Interfaces: Create generative AI interfaces that allow users to query, interrogate, and extract granular insights from dense, structured, and unstructured documentation.

 2. Intelligent Data Extraction & Query Pipelines
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Query Augmentation: Build pipelines that automatically inject external metadata and ontologies into LLM prompts to maximize query accuracy.
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Dual-Stream Parsing: Code high-precision extraction strategies to ingest and parse data from both modern structured formats and legacy unstructured documents.
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Similarity Matching: Develop algorithmic workflows to compare newly processed documents against historical databases using metadata clustering and vector similarity., Confidence Scoring: Design and implement automated confidence scoring mechanisms and LLM-as-a-judge frameworks to estimate the accuracy of query results and proactively alert users when manual review is needed.
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Feedback Loops: Program feedback processes to capture user input and error patterns, enabling continuous model, prompt, and routing improvement.
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Extensible Documentation: Document architectural patterns, lessons learned, and framework constraints to allow the methodology to scale across other business units and regulatory review streams., Audit Trail Architecture: Implement comprehensive, stateful logging across all multi-agent steps, ensuring every data point extracted or populated into user dashboards can be traced back to its exact source snippet in the original documentation.
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PII & Data Privacy Guardrails: Design and embed automated preprocessing layers to detect, redact, or safely handle Personally Identifiable Information (PII) and sensitive corporate data before it is processed by external LLM APIs.
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Enterprise Security Compliance: Ensure all agentic pipelines, vector databases, and Model Context Protocol (MCP) integrations strictly adhere to enterprise data isolation, encryption-at-rest, and encryption-in-transit protocols.
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Access Control & Permissions: Implement secure role-based data routing within the agent logic, ensuring the AI system only retrieves and displays information that the querying user has explicit permission to view.


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