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# Data Scientist / AI Architect (Agentic AI & LLM Focus)

- **Company:** Cardinal Integrated Technologies Inc
- **Location:** Irvine, CA, United States
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Engineering, Software Quality, Information Engineering, Identity and Access Management, Python (Programming Language), Machine Learning, Performance Tuning, Data Logging, Enterprise Software Applications, ReactJS, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Model Validation, Caching, Front End Software Development, Data Pipelines
- **Published:** September 17, 2026
- **Apply:** https://www.dice.com/job-detail/11e90383-1c5d-46b6-89f4-8c4f58676b30

## About the Role

* Hands-on experience in AI/LLM solution design and implementation
 * Strong understanding of AI/ML/LLM libraries used in projects
 * Experience with LLM fine-tuning (critical requirement)
 * Experience in RAG (Retrieval-Augmented Generation) architectures

## Description

We are engaging a hands-on Data Scientist / AI Architect to design and deliver agent-based, AI-enabled workflows integrated with enterprise systems. The role requires close collaboration with internal teams and business stakeholders to translate use cases into scalable, production-grade solutions.

Core Responsibilities

Data Science & Agent-Oriented System Design

 * Design, develop, and deploy Python-based data science solutions supporting:

 * Agent-driven workflows (supervisor/sub-agent architectures, intelligent decision systems)
 * Data pipelines, APIs, and enterprise system integrations for model deployment
 * Multi-step, asynchronous processing and experimentation workflows

Apply strong data science and engineering practices, including:

 * Model validation and evaluation
 * Testing and reproducibility
 * Code quality, performance optimization, and error handling

AI / LLM-Enabled Solution Development

 * Design and implement end-to-end LLM-powered solutions, including:

 * Prompt engineering and context management to optimize model performance
 * Structured output generation, validation, and post-processing for reliable outcomes

Integrate LLMs into analytical pipelines and decision-making workflows

Stakeholder Collaboration

 * Work closely with business stakeholders to:

 * Translate business use cases into technical designs and acceptance criteria
 * Communicate trade-offs across quality, cost, risk, and delivery timelines

Good to Have

Data Engineering for Retrieval-Based Systems

 * Design and manage retrieval pipelines to support grounding and context enrichment, including:

 * Vector databases and similarity search
 * Search and indexing systems
 * Storage solutions for source data and embeddings
 * Caching strategies for performance and scalability

Cloud-Native Delivery (AWS Preferred)

 * Deploy and manage AI/ML solutions on cloud platforms, with focus on:

 * IAM and security best practices
 * Scalability, resilience, and availability
 * CI/CD pipelines and environment management

Integration & UX Enablement

 * Integrate AI solutions with enterprise tools via secure APIs and gateways

 * Collaborate with front-end teams (e.g., React) to enable seamless user experiences

Observability & Operations

 * Implement monitoring across workflows, including:

 * Logging, metrics, and tracing for agent pipelines and model calls


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