LEAD DATA SCIENCE (ML Ops)

Engineering and DevOps • Strong Python; clean, tested, production-grade code • Infrastructure as Code (Terraform strongly preferred)

Job Responsibilities • Architect and evolve the ML platform on Databricks (Unity Catalog, MLflow, Feature Store, Model Serving, Workflows) integrated with AWS services • Define and enforce standards for CI/CD, testing, model promotion, and reproducibility across teams

• Cluster configuration, policies, and performance tuning AWS • Core services: S3, IAM, VPC, EC2, ECR, Lambda, CloudWatch, Secrets Manager, KMS

• Core services: S3, IAM, VPC, EC2, ECR, Lambda, CloudWatch, Secrets Manager, KMS • Databricks-on-AWS networking and security (PrivateLink, cross-account roles, workspace setup) • Familiarity with SageMaker, EKS, or API Gateway as complements to Databricks serving

• Feature store design and streaming (Structured Streaming, Must have: Kafka/Kinesis) • Databricks or AWS certifications (ML Professional, Solutions Architect) • FinOps experience and regulated-industry or data-residency exposure

• Infrastructure as Code (Terraform strongly preferred) • CI/CD (GitHub Actions, GitLab CI, Jenkins, or Azure DevOps) • Docker; Kubernetes is a must

Required Skills Databricks • Production experience with MLflow (tracking, registry, model serving), Unity Catalog, Delta Lake, and Databricks Workflows/Jobs

Databricks • Production experience with MLflow (tracking, registry, model serving), Unity Catalog, Delta Lake, and Databricks Workflows/Jobs • Databricks Asset Bundles or Terraform provider for workspace and job deployment

• Production experience with MLflow (tracking, registry, model serving), Unity Catalog, Delta Lake, and Databricks Workflows/Jobs • Databricks Asset Bundles or Terraform provider for workspace and job deployment • Spark/PySpark for scalable data and feature pipelines

• Databricks-on-AWS networking and security (PrivateLink, cross-account roles, workspace setup) • Familiarity with SageMaker, EKS, or API Gateway as complements to Databricks serving Engineering and DevOps

• Experiment tracking, model versioning, and reproducibility • LLMOps on Databricks: Mosaic AI, Vector Search, RAG pipelines, evaluation and guardrails • Feature store design and streaming (Structured Streaming, Must have: Kafka/Kinesis)

• CI/CD (GitHub Actions, GitLab CI, Jenkins, or Azure DevOps) • Docker; Kubernetes is a must MLOps practices

• Build automated pipelines for training, validation, deployment, and retraining • Implement monitoring for model performance, data drift, latency, and cost (Lakehouse Monitoring, CloudWatch, Prometheus/Grafana, or similar) • Own governance: model lineage, access control, audit trails, and compliance with data and regulatory requirements

• LLMOps on Databricks: Mosaic AI, Vector Search, RAG pipelines, evaluation and guardrails • Feature store design and streaming (Structured Streaming, Must have: Kafka/Kinesis) • Databricks or AWS certifications (ML Professional, Solutions Architect)

AWS • Core services: S3, IAM, VPC, EC2, ECR, Lambda, CloudWatch, Secrets Manager, KMS • Databricks-on-AWS networking and security (PrivateLink, cross-account roles, workspace setup)

• Databricks Asset Bundles or Terraform provider for workspace and job deployment • Spark/PySpark for scalable data and feature pipelines • Cluster configuration, policies, and performance tuning

• Strong Python; clean, tested, production-grade code • Infrastructure as Code (Terraform strongly preferred) • CI/CD (GitHub Actions, GitLab CI, Jenkins, or Azure DevOps)