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.

Experience
4-6 yrs