Senior Data Scientist - AI​/ML Engineer

Who are we?

Altimate AI, founded in 2022 in San Francisco, is revolutionizing enterprise data operations through the power of AI. Our mission is to alleviate the burden on overworked and understaffed enterprise data teams by providing innovative AI‑driven solutions that automate and accelerate a wide range of data tasks. Our flagship product, Data Pilot, offers advanced data automation capabilities, while our new Data Mates technology brings the concept of agentic AI to data operations, acting as virtual teammates for data professionals.

Our solutions seamlessly fit into existing tools like VSCode, Git, and Slack, performing tasks ranging from data documentation to performance optimization.

By leveraging a proprietary framework that combines multiple language models and a custom‑built knowledge graph, we enable contextually aware AI agents that integrate seamlessly into existing workflows. Our solutions, including ambient AI for continuous monitoring and optimization, are designed to meet the growing demands of data operations, business intelligence, and analytics in an era of ever‑increasing data volumes. Used by thousands of users across the world, and backed by prominent investors, we're positioned at the forefront of the AI‑powered data engineering revolution.

You can read more about us in a recently published venture beat article.

As a team, we are Silicon Valley veterans who previously created category‑defining data and AI products loved by thousands of companies worldwide. We have experienced the journey of a small startup to IPO very closely. We have started on a similar journey again and are backed by prominent advisors and VC firms with multi‑billion dollar portfolios.

We're in search of a Senior Generative AI Engineer who brings deep expertise in building and deploying large language models and AI systems s role is for those with extensive experience in developing production‑grade AI solutions, particularly in the realm of generative AI and language models. The ideal candidate will play a crucial role in advancing our AI capabilities, designing intelligent agent architectures, and implementing sophisticated RAG pipelines that power our products used by thousands of data teams worldwide.

• 5+ years of hands‑on ML/AI experience with at least 1 year of hands‑on experience in GenAI/LLM projects

• Track record of successfully deploying ML/AI systems in production environments

• Experience working with enterprise customers and understanding their unique requirements

• Strong programming skills in Python, including expertise in ML stack (Hugging Face, Num Py, Pandas, scikit‑learn)

• Production experience with deep learning frameworks (PyTorch/Tensorflow) and transformer architectures

• API development expertise (FastAPI) and production deployment experience

• Proven experience with prompt engineering best practices and LLM evaluation metrics

Infrastructure & Deployment

• Experience with vector databases (e.g., Pinecone, Weaviate) and RAG pipelines

• Experience with observability and monitoring of LLM applications

• Proficiency in model compression, distillation, and deployment at scale

Model Development & LLMs

• Expertise in semantic search and embedding models

• Experience in LLM frameworks (Lang Chain, Llama Index) and agentic AI systems

• Advanced model fine‑tuning techniques, including LoRA, Prompt Tuning, and Adapter Training

• Advanced AI Agent Architecture
  :
  Design and implement sophisticated multi‑agent systems that can understand context, make decisions, and enable seamless collaboration between AI and human data professionals.

• Large‑Scale RAG Systems
  :
  Build and optimize retrieval‑augmented generation pipelines that can efficiently process and utilize enterprise‑scale knowledge bases.

• Model Optimization & Deployment
  :
  Implement advanced techniques (LoRA, Prompt Tuning) to adapt off‑the‑shelf models for specific enterprise data tasks, ensuring faster, more accurate performance.

• Scalable AI Infrastructure: Develop and optimize cloud‑native architectures (AWS, Kubernetes)…