Description

Key Responsibilities

Standardize and enhance MLOps and LLMOps workflows across multiple teams and business units.

Design, develop, and optimize CI/CD pipelines for machine learning and Generative AI applications.

Deploy, monitor, and manage ML and LLM models in production environments.

Establish best practices for model lifecycle management using MLflow, including governance, observability, and compliance.

Build scalable infrastructure to support model deployment, monitoring, and release management.

Collaborate closely with data science, platform engineering, and software engineering teams to advance enterprise AI initiatives.

Drive automation efforts and implement Infrastructure as Code (IaC) practices for ML platform operations.

Support production-grade GenAI and LLM systems with a focus on reliability, scalability, and operational efficiency.

Lead technical initiatives to define scalable MLOps standards and enterprise best practices.

Required Qualifications

Strong hands-on experience with Databricks and MLflow.

Proven experience building, scaling, and maintaining MLOps / LLMOps platforms.

Strong expertise in Azure and/or GCP cloud platforms.

Hands-on experience developing CI/CD pipelines and automation frameworks.

Strong understanding of model deployment, monitoring, and lifecycle management.

Experience with Kubernetes, Docker, and Infrastructure as Code (Terraform preferred).

Experience supporting GenAI / LLM applications in production environments.

Knowledge of model evaluation, observability, governance, compliance, and release management.

Excellent collaboration and communication skills in cross-functional engineering environments.

Preferred Qualifications

Experience working with enterprise-scale AI/ML platforms.

Exposure to production-grade LLM systems, monitoring frameworks, and operational best practices.

Demonstrated ability to lead technical initiatives and establish scalable engineering standards

Key Skills

Education

Bachelor's degree