Job Title: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
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Must Have Skills
• Skill 1 – Python & Backend Development
• Skill 2 – LLM / Generative AI Application Development
• Skill 3 – Agent-Based AI Systems
• Skill 4 – RAG / Vector DB / Embeddings
• Skill 5 – API Development & System Integration
• Skill 6 – AWS Cloud (Cloud-Native Development)
• Skill 7 – CI/CD & DevOps Practices
• Skill 8 – Observability (Logging, Monitoring, Tracing)
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Domain Experience (If any)
• AI-enabled enterprise workflows
• Marketing / Content generation platforms (nice to have)
• Financial / Investment domain (preferred based on content use cases)
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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