AI Lead Engineer / AI Architect (Hands-on)

Position: AI Lead Engineer / AI Architect (Hands-on)

Location: Irvine, CA

Duration: 6 Months

Job Type: Temporary Assignment

Work Type: Hybrid

Job Description:

You Are:

• A highly skilled hands-on AI Lead Engineer / Architect with at least 8+ years of experience in AI/ML, Data Engineering, 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.

The 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