Director, Data Engineer (Python, Pyspark, MLOPs, LLMOPs, SQL, Databricks)

Architect and lead the development of scalable, high-volume data pipelines to support model training and multi-prompt inference workflows at enterprise scale.

Design and implement evaluation frameworks, guardrails, and automated quality gates to ensure reliability and performance of AI outputs in production.

Establish and oversee MLOps/LLMOps practices, telemetry systems, and CI/CD governance to scale complex AI workflows and modernize production data platforms.

9+ years of progressive experience in data engineering, advanced analytics, MLOps, and distributed system architecture.

Bachelor’s degree required; Master’s degree preferred in Computer Science, Data Science, Software Engineering, or related quantitative field.

Strong proficiency in Python and experience with modern AI/ML pipeline architecture, deployment, monitoring, and cloud data platforms.

Experience designing and operating MLOps/LLMOps systems, evaluation tooling, system monitoring, and automated quality control for AI models.

Strategic technical leader focused on building scalable, reusable architectures rather than isolated implementations.

Experienced in translating loosely defined prototypes and requirements into actionable, production-grade engineering solutions.

Comfortable operating in a fast-paced, high-growth environment balancing multiple priorities, technical debt, and architectural trade-offs.