Title: AI Sr. Application Engineer

Location: Bay area, CA

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

Total IT 10+ 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 Proficient Nice to have

AI Safety & Guardrails

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

AI Sr. Application Engineer

Quick Overview