Sr AI Architect

Job Title: Sr AI Architect

Location: Bay area, CA  (need only local candidates)

Duration: Fulltime Permanent

Total 15+ Years Experience

·      Will work on the intelligence layer for multiple programs — owns all model quality, RAG accuracy, prompt engineering, and AI safety across applications.

·      Socratic tutor persona, adaptive learning recommendation engine, multi-modal AI (text and voice), RAG evaluation framework, and feedback loop into retrieval.

·      6-LLM call chain orchestration (NeMoGuardrails → intent classification → query rewriting → RAG → synthesis), , and compatibility check logic.

·      Production-grade AI quality from launch — this is not a research or prototyping role; accuracy thresholds, latency requirements, and safety guardrails must pass InfoSec adversarial testing before Release 1.

Experience

Total IT 15+ Years

·      4–7 years of software engineering with at least 2 years focused on LLM application development in production — not research, not demos, not internal tools with 10 users

·      Has shipped an LLM-powered feature or product to production where real users depend on the accuracy and the engineer owns the quality metrics

·      Has owned an AI safety or guardrails implementation for a customer-facing product — not just added an off-the-shelf filter; designed and tested the safety layer

·      Has built RAG evaluation pipelines and used them to make go/no-go release decisions — accuracy gating is part of the workflow.

·      Has profiled and optimized a multi-step LLM call chain for latency.

LLM Application Development

·      LLM prompt engineering — system prompts, few-shot examples, chain-of-thought, instruction following · Expert · Must-have

·      Multi-step LLM chain orchestration — LangChain, LlamaIndex, or custom orchestration · Expert · Must-have

·      Multi-turn conversation design — context window management, conversation summarization, session memory · Advanced · Must-have

·      Streaming LLM response handling — token-by-token streaming, partial response rendering · Advanced · Must-have

·      Model selection and benchmarking — matching model size to task; balancing latency, cost, and accuracy · Advanced · Must-have

RAG Pipeline Design & Quality

·      RAG pipeline design — chunking strategy, embedding model selection, retrieval configuration · Expert · Must-have

·      Vector similarity search tuning — index parameters, similarity thresholds, retrieval depth · Advanced · Must-have

·      Reranking — cross-encoder rerankers, relevance scoring · Advanced · Must-have

·      RAG evaluation frameworks — RAGAS, TruLens, or equivalent; automated eval pipelines · Advanced · Must-have

·      Hybrid search — combining dense vector retrieval with BM25 or keyword search ·

AI Safety & Guardrails

·      Prompt injection detection and mitigation · Advanced · Must-have

·      Jailbreak testing and red-teaming LLM systems · Advanced · Must-have

·      Content safety classifier integration · Advanced · Must-have

·      Hallucination detection and mitigation strategies · Advanced · Must-have

·      Topical control — enforcing scope boundaries on LLM responses · Advanced · Must-have.

Evaluation & Production Quality

·      Automated evaluation pipeline design — test set curation, metric selection, regression detection · Advanced · Must-have

·      A/B evaluation methodology for prompt and model changes · Proficient · Must-have

·      Latency profiling for LLM call chains — identifying bottlenecks across multi-step pipelines · Proficient · Must-have

·      Feedback loop design — user signal collection, signal-to-retrieval-weight integration · Proficient · Must-have

·      Production model monitoring — accuracy drift detection, quality degradation alerting Proficient · Must-have

Development

·      Python — ML/AI application development, async programming · Expert · Must-have

·      API design for AI services — streaming endpoints, error handling, timeout management · Advanced · Must-have

·      Embedding model operations — model selection, batch embedding, index updates · Advanced · Must-have

Nice to Have

·      Adaptive learning systems or personalization engine experience

·      Knowledge graph integration with RAG

·      Multi-agent orchestration patterns

·      ServiceNow API integration

·      Prior experience building AI products on NVIDIA infrastructure