Job Title: Data Scientist / AI Architect (Agentic AI & LLM Focus)
Duration: 6-12+ Months Contract
Location: Irvine, CA - Onsite
Context & Objective
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
- Support performance tuning, incident diagnosis, and continuous optimization
Interview Focus Areas
- 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
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