AI – Sr. Application Engineer

About the role

Will work on the intelligence layer for multiple programs, owning all model quality, RAG accuracy, prompt engineering, and AI safety across applications including Socratic tutor persona, adaptive learning recommendation engine, multi-modal AI (text and voice), RAG evaluation framework, and feedback loop into retrieval. Responsibilities include LLM call chain orchestration (NeMoGuardrails, intent classification, query rewriting, RAG synthesis) and compatibility check logic. This is a production-grade AI quality role from launch—not research or prototyping. Accuracy thresholds, latency requirements, and safety guardrails must pass InfoSec adversarial testing before Release 1.

Required 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

• 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