AI – Sr. Principal AI Solutions Architect -Bay area- CA

Job Title: Principal AI Solutions Architect Location: Santa Clara, CA Duration: FTE 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 Required Skills Experience Prefer total IT 15+ Years Prefer 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 · Proficient · Nice to have 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 Show more Show less