Job Type: Direct Hire
Pay Range: $300,000–$350,000 Total Compensation (Salary, Bonus, Stocks, RSUs) + benefits
Start Date: ASAP
Location: Hybrid - 3 days onsite in San Francisco, CA
About the Opportunity:
Our client, a leader in SaaS and AI-powered intelligence, is looking for a skilled AI/ML Engineer to join their team. This project involves launching a greenfield AI initiative to completely rebuild their enterprise sales and talent intelligence platform from the ground up with an AI-first architecture and no legacy constraints. This is a high-impact role in a 0-to-1 product environment that requires a self-motivated professional who can hit the ground running and deliver results quickly.
Key Responsibilities & Deliverables:
This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include:
- Build and deploy end-to-end LLM pipelines and AI features into production.
- Design and optimize RAG architectures using vector databases such as Pinecone, FAISS, and Weaviate at scale.
- Develop agentic systems using frameworks like LangGraph, LlamaIndex, or similar, including tool use, multi-agent coordination, and reasoning loops.
- Own prompt engineering, model versioning, evaluation (e.g., RAGAS, DeepEval), and LLMOps instrumentation.
- Integrate AI capabilities into large-scale data pipelines while ensuring observability and production guardrails.
- Collaborate with leadership and an engineering team across global locations.
We are looking for someone with a proven track record of successful engagements. The ideal candidate will have:
- 3–5 years of AI/ML engineering experience, including at least 2 years building LLM-powered systems in production.
- BS or MS in Computer Science, Machine Learning, or a related field.
- Deep expertise in Python, with experience in PyTorch or Hugging Face Transformers.
- Experience shipping LLM-powered applications (RAG, fine-tuning, agents) to production.
- Familiarity with tools like LangChain/LlamaIndex, vector DBs, and cloud infrastructure.
- Experience with cloud platforms such as AWS or GCP, and containerization tools like Docker and Kubernetes.
- A track record of building in startup or high-ambiguity environments.
- Demonstrated portfolio of shipped AI solutions, including agentic pipelines, RAG systems, or fine-tuned models.
- Strong communication skills to provide clear and concise status updates to the project team.
