Lead Data Scientist & Machine Learning Engineer

Lead Data Scientist & Machine Learning Engineer

Location:

Houston, TX or SFO, CA or Remote

Long Term

About the Role

Are you a visionary in the world of

Generative AI

and

Databricks

? We are looking for a

Lead Data Scientist & ML Engineer

to bridge the gap between cutting-edge research and scalable production systems.

In this role, you won’t just be building models; you’ll be architecting the future of our AI ecosystem. You will lead the charge in leveraging the

Databricks Data Intelligence Platform

to build, deploy, and monitor sophisticated ML and GenAI solutions that solve complex business problems.

What You’ll Do

• Architect GenAI Solutions:

Design and implement LLM-based applications using RAG (Retrieval-Augmented Generation), fine-tuning, and prompt engineering.

• End-to-End ML Ops:

Build and automate robust ML pipelines on Databricks, utilizing

Unity Catalog

,

MLflow

, and

Model Serving

.

• Lead & Mentor:

Act as the technical North Star for a team of data scientists and engineers, fostering a culture of excellence and rapid experimentation.

• Productionize at Scale:

Convert POCs into enterprise-grade products, ensuring high performance, low latency, and cost-efficient scaling.

• Cross-Functional Collaboration:

Partner with Stakeholders, Data Engineers, and DevOps to align AI initiatives with core business objectives.

What We’re Looking For

• Experience:

6+ years in Data Science/ML Engineering, with at least 2 years in a leadership or principal capacity.

• Databricks Mastery:

Expert-level knowledge of the Databricks ecosystem (

Delta Lake, Spark, Mosaic AI, Workflows

).

• Generative AI Expertise:

Hands-on experience with frameworks like

LangChain

,

LlamaIndex

, and Vector Databases (e.g., Pinecone, Weaviate, or Databricks Vector Search).

• Technical Stack:

• Languages:

Python (Expert), SQL, PySpark.

• ML Frameworks:

PyTorch, TensorFlow, or Scikit-learn.

• DevOps/MLOps:

Experience with CI/CD, MLflow, and containerization (Docker/Kubernetes).

• Education:

Master’s or PhD in Computer Science, Mathematics, Statistics, or a related quantitative field (or equivalent experience).