Machine Learning Engineer

Responsibilities:

• Build and scale customer data science workloads, applying MLOps best practices to productionize models across diverse domains.
• Develop LLM solutions on customer data, including RAG architectures over enterprise knowledge repositories, natural-language querying of structured data, and content generation.
• Advise data teams on architecture, tooling, and best practices across the data science lifecycle.

Requirements:

• 4-6 years (Senior ML Engineer) or 6+ years (ML Architect) of hands-on industry data science experience with pandas, MLflow, scikit-learn, gensim, NLTK, and TensorFlow/PyTorch.
• Proven experience deploying production-grade ML on AWS, Azure, or GCP, including drift monitoring.
• Databricks Certification or an ML/Cloud AI Certification required, or 1+ years of current, hands-on Databricks experience instead of certification.
• Graduate degree in a quantitative discipline (CS, engineering, statistics, or operations research) or equivalent practical experience.

Nice to Have:

• Apache Spark experience at scale.
• Deep Databricks platform expertise.