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AI Scientist / AI Architect (Hands-On) (Hybrid, 4 days onsite) (4 open roles)
Location: Irvine, CA 92618 (3 days) & Los Angeles, CA (1 day) 90013
Length: 6 month contract (potential extension)
Overview:
A highly skilled, hands-on AI Scientist / Architect with at least 8+ years of experience in AI/ML, Data Science, or Software Engineering. You bring strong expertise in designing and building scalable, production-ready AI solutions, with deep hands-on experience in LLM-enabled applications, agent-based systems, and cloud-native architectures. You are comfortable working closely with business stakeholders and leading AI-driven innovation initiatives in an enterprise environment.
Responsibilities:
- Design and develop scalable AI/ML pipelines and intelligent applications aligned with enterprise standards
- Build agent-based AI workflows, automation systems, and retrieval-based architectures including RAG, vector search, and embeddings
- Architect and implement LLM orchestration layers supporting content ideation, drafting, and editing workflows
- Lead integration of AI solutions with backend systems and enterprise platforms including APIs, internal tools, and data platforms
- Partner with product, marketing, and business stakeholders to translate requirements into AI-driven solutions
- Provide architectural leadership, guide offshore teams, and ensure delivery aligned with scalability, security, and governance standards
What You Need:
- At least 8+ years of experience in AI/ML, Data Science, or Software Engineering
- Strong Python backend development experience
- Hands-on experience with LLM-enabled applications and Generative AI
- Experience building agent-based and agent-oriented AI systems
- Strong expertise in retrieval-based systems including RAG, vector databases, embeddings, and indexing
- Experience with API development and backend system integration
- AWS cloud-native development experience
- Experience with CI/CD pipelines and environment management
- Strong understanding of observability including logging, monitoring, and tracing
- Experience deploying ML models in production environments
- Exposure to enterprise AI workflows, automation, and governance models