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 & Dev Ops 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