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Remote, Oregon, United States
Remote only
As a Lead AI Engineer, you will be at the forefront of our Generative AI initiatives. We treat AI as a software engineering discipline. You will be responsible for the full lifecycle of our AI features—specifically document intelligence and RAG pipelines—taking them from initial prototype to robust, scalable production services. You will solve for real-world constraints like latency, error handling, and cost optimization.
You’ll collaborate with a diverse range of clients to translate business needs into high-performance AI architectures. This role requires a blend of deep technical expertise in LLMs and a disciplined Software Engineering approach to ensure our solutions are robust, ethical, and scalable.
Engineer for Precision: Develop advanced RAG (Retrieval-Augmented Generation) pipelines and Semantic Search systems using Google Cloud Vector Search or Pinecone
Optimize Models: Lead efforts in LLM and Embedding fine-tuning to improve domain-specific performance
Agentic Ops: Build and manage agentic workflows that automate complex multi-step reasoning tasks
Collaborate & Innovate: Work directly with customers to understand requirements, suggest novel features, and implement state-of-the-art AI techniques
Productionize: Apply MLOps best practices to ensure models are served efficiently, monitored, and continuously improved
AI/LLM Ecosystem: Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning
Data & Search: Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search
Infrastructure: Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures
Frameworks: Proficiency in LangChain, LlamaIndex, or similar orchestration layers
Mindset: A strong software engineering foundation—you write clean, maintainable code and understand the full SDLC
8+ years of experience in AI/ML
Proven track record of deploying GenAI products to a production environment
Experience with Classic Machine Learning (neural nets, training, tuning) is a strong plus
Knowledge of Data Engineering and SQL
Curiosity: The AI landscape changes weekly; you are a lifelong learner who stays ahead of the curve
Consultative Spirit: You enjoy interacting with clients and can translate technical complexity into business value
Ethics: You prioritize responsible AI development and data privacy
