AI Architect
On-siteCarlsbad, California, United States
Job Summary
Architect end-to-end LLM solutions for chatbot applications, semantic search, and domain-specific assistants using Databricks Unity Catalog for centralized governance, metadata management, and audit trails. Design modular, scalable workflows including prompt orchestration, RAG, and real-time inference pipelines while integrating feedback loops to continuously refine model performance. Optimize latency and cost through fine-tuning and token management, overseeing secure production deployments with strict access controls and compliance alignment. Lead teams on data quality and responsible AI practices across enterprise-scale use cases.
Required Qualifications
- Currently based in CA
- 7+ years in AI/ML solution architecture
- 2+ years focused on LLMs and Generative AI
- Strong experience working with OpenAI (GPT-4/o), Claude, Gemini, and integrating LLM APIs into enterprise systems
- Proficiency in Databricks, including Unity Catalog, Delta Lake, MLflow, and cluster orchestration
- Deep understanding of data governance, metadata management, and data lineage in large-scale environments
- Hands-on experience with chatbot frameworks, LLM orchestration tools (LangChain, LlamaIndex), and vector databases (e.g., FAISS, Weaviate, Pinecone)
- Strong Python development skills, including notebooks, REST APIs, and LLM orchestration pipelines
- Ability to map business problems to AI solutions, with strong architectural thinking and stakeholder communication
- Familiarity with feedback loops and continuous learning patterns (e.g., RLHF, user scoring, prompt iteration)
- Experience deploying models in cloud-native and hybrid environments (AWS, Azure, or GCP)
Desired Qualifications
- Experience fine-tuning or optimizing open-source LLMs (e.g., LLaMA, Mistral) with tools like LoRA/QLoRA
- Knowledge of compliance requirements (HIPAA, GDPR, SOC2) in AI systems
- Prior work building secure, governed LLM applications in highly regulated industries
- Background in data cataloging, enterprise metadata management, or ML model registries
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