Role: Data Scientist / AI Architect (Agentic AI & LLM Focus)
Location: 2-3 days / week in the client s Irvine office, 1 day in their downtown LA office, 1 day remote
Onsite: Yes
Rate:$90/Hr On C2C
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:
o Agent-driven workflows (supervisor/sub-agent architectures, intelligent decision systems)
o Data pipelines, APIs, and enterprise system integrations for model deployment
o Multi-step, asynchronous processing and experimentation workflows
Apply strong data science and engineering practices, including:
o Model validation and evaluation
o Testing and reproducibility
o Code quality, performance optimization, and error handling
AI / LLM-Enabled Solution Development
Design and implement end-to-end LLM-powered solutions, including:
o Prompt engineering and context management to optimize model performance
o 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:
o Translate business use cases into technical designs and acceptance criteria
o 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:
o Vector databases and similarity search
o Search and indexing systems
o Storage solutions for source data and embeddings
o Caching strategies for performance and scalability
Cloud-Native Delivery (AWS Preferred)
Deploy and manage AI/ML solutions on cloud platforms, with focus on:
o IAM and security best practices
o Scalability, resilience, and availability
o 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:
o Logging, metrics, and tracing for agent pipelines and model calls
Support performance tuning, incident diagnosis, and continuous optimization
Screening / 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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