AI Scientist / AI Architect (Hands-on)

AI Scientist / AI Architect (Hands-on)

Location: Irvine, CA Onsite Requirement: Yes Number of days onsite: 2-3 days/week in the client's Irvine office, 1 day in their downtown LA office, 1 day remote Mandatory Areas: AI/ML Solution Architecture, LLM & Generative AI Development, Agent-Based Systems, Retrieval-Augmented Systems (RAG), Enterprise AI Integration

Must Have Skills: Python & Backend Development, LLM / Generative AI Application Development, Agent-Based AI Systems, RAG / Vector DB / Embeddings, API Development & System Integration, AWS Cloud (Cloud-Native Development), CI/CD & DevOps Practices, Observability (Logging, Monitoring, Tracing)

Domain Experience (If any): AI-enabled enterprise workflows, Marketing / Content generation platforms (nice to have), Financial / Investment domain (preferred based on content use cases)

Must Have Certifications: Not mandatory (AWS / ML / AI certifications are good to have)

Opportunity:

• 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 (RAG, vector search, 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 (APIs, internal tools, 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 / agent-oriented AI systems
• Strong expertise in retrieval-based systems (RAG, vector databases, embeddings, 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 (logging, monitoring, tracing)
• Experience deploying ML models in production environments
• Exposure to enterprise AI workflows, automation, and governance models