ML Architect + Databricks

Role & responsibilities

• 79 years of overall Data Science / Machine Learning experience.

• 4+ years of Databricks experience.

• Strong Python development experience.

• Strong experience with:

• Pandas

• Scikit-learn

• MLflow

• TensorFlow

• PyTorch

• Gensim

• NLTK

• Experience building and deploying production-grade ML solutions.

• Strong understanding of MLOps.

• Hands-on model monitoring and ML drift monitoring.

• Experience with at least one major cloud platform:

• AWS

• Azure

• GCP

GenAI / LLM Experience

The candidate should have hands-on experience with:

• LLMs
• RAG
• Vector databases
• Embeddings
• Semantic search
• LLM fine-tuning
• LLM deployment
• Hugging Face
• LangChain
• OpenAI
• Prompt engineering
• Natural-language-to-SQL
• Enterprise knowledge repositories
• LLM evaluation and monitoring

Databricks Experience

Look for candidates with practical experience in:

• Databricks platform
• Databricks ML
• MLflow
• Apache Spark
• PySpark
• Distributed data processing
• ML pipelines
• Databricks Jobs/Workflows
• Model deployment
• Lakehouse architecture

Bonus: Delta Lake, Unity Catalog, Databricks SQL, Feature Store/Feature Engineering, and Databricks Model Serving.

Preferred candidate profile

The ideal candidate is a Senior ML/AI Architect with 79 years of overall experience, including 4+ years of hands-on Databricks experience, strong production ML/MLOps expertise, and practical experience delivering LLM, RAG, and Generative AI solutions.

The candidate should be able to work across the complete lifecycle—from business requirement ML architecture model development MLOps deployment monitoring optimization.