AI Sr. Application Engineer

Job Title: AI Sr. Application Engineer

Location: Bay area, 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

• 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 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 - 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

• 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

• 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

• 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

• 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