Senior AI Application Engineer

McClure Engineering Co.
San Francisco, CA, United States
12 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
1 year minimum
Compensation
$150,000.0 - $170,000.0
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Graph Database Python (Programming Language) Recommender Systems Software Safety Search Technologies Software Engineering Retrieval-Augmented Generation Large Language Models Multi-Agent Systems Prompt Engineering Generative AI
+3 more
Build Management Api Design Servicenow

Job description

  • Design and build enterprise LLM applications
  • Develop scalable RAG pipelines and evaluation frameworks
  • Optimize prompt engineering, retrieval quality, and model accuracy
  • Implement AI safety, guardrails, hallucination detection, and security controls
  • Build multi-step LLM orchestration workflows
  • Improve latency, scalability, and production reliability
  • Collaborate with product, platform, and engineering teams to deliver AI-powered solutions

Why Join?

  • Build next-generation enterprise AI products
  • Work on LLMs, RAG, Multi-Agent AI, and Generative AI technologies
  • Solve challenging AI quality, safety, and scalability problems
  • Collaborate with experienced AI engineers and product leaders
  • Opportunity to shape production AI systems used by real customers

Pay: $150,000.00 - $170,000.00 per year

Requirements

We are seeking a Senior AI Application Engineer with deep expertise in LLMs, RAG, Prompt Engineering, AI Safety, and Production AI Systems. You will build and own enterprise-grade AI applications that power intelligent learning experiences, conversational AI, adaptive recommendation engines, and multi-modal AI solutions. This is a production engineering role focused on building reliable, scalable, and secure AI products-not research or proof-of-concepts., * 10+ years of overall software engineering experience

  • 2+ years building production-grade LLM applications
  • Strong expertise in Python
  • Hands-on experience with RAG (Retrieval-Augmented Generation)
  • Prompt Engineering and LLM optimization
  • LangChain, LlamaIndex, or custom orchestration frameworks
  • AI Safety, Guardrails, Prompt Injection & Jailbreak Prevention
  • RAG Evaluation Frameworks (RAGAS, TruLens, or equivalent)
  • Vector Databases, Embeddings, and Semantic Search
  • Production AI monitoring, evaluation, and optimization
  • API development and AI service deployment

Preferred Skills

  • NVIDIA AI ecosystem
  • NeMo Guardrails
  • Knowledge Graphs
  • Multi-Agent AI Systems
  • Adaptive Learning Platforms
  • ServiceNow API Integration
  • Hybrid Search (BM25 + Vector Search), * production LLM applications: 1 year (Preferred)

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