What are the responsibilities and job description for the AI Lead Engineer / AI Architect (Hands-on) position at Arrowminds inc?
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