Machine Learning Engineering Development Senior Lead

Key Skills

AWS Sagemaker, Infrastructure as code, Lambda, Leadership Skill, Machine learning techniques, MLOps tools, Python Programming, Scala Programming, SPARK Programming, Terraform

Job Description

A seasoned MLOps professional to lead and drive the operationalization, management, and continuous improvement of our Al/ML pipelines. This role involves working closely with data scientists and engineers to ensure models are deployed reliably, monitored effectively, and optimized for maximum business impact.

ESSENTIAL DUTIES AND RESPONSIBILITIES

Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time. 

• Pipeline Automation: Develop and maintain automated Cl/CD pipelines for machine learning models, ensuring seamless transitions from development to production in AWS/on-premise environments.

• Model Deployment & Scaling: Build and manage strategies to deploy models utilizing SageMaker (must-have), Bedrock, and other AWS services, ensuring scalability and performance.

• Monitoring & Retraining: Implement comprehensive monitoring of deployed models, establishing alerts and triggers for proactive maintenance and retraining to address performance drift.

• Version Control & Experimentation: Implement a streamlined workflow using version control systems (e.g., GitHub, Bitbucket) for code and models, integrated with container repositories (e.g., Docker Hub, AWS ECR) and model repositories (e.g., Sagemaker Model Registry) to enable robust production deployments

• Cross-Team Collaboration: Liaise with data scientists, engineers, and business stakeholders to streamline MLOps processes and ensure models meet business objectives.

• MLOps Excellence: Implement and streamline MLOps processes to continuously enhance models, ensuring peak performance at scale.

QUALIFICATIONS

Required Qualifications:

The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• Education: Bachelor's degree in Computer Science, Software Engineering, or a related field.

• Experience: Minimum of 15+ years in software development, with 5+ years focused on MLOps practices.

• Proven experience working within the financial services industry.

• Technical Skills: In-depth knowledge of Python, Spark, Scala, MLOps tools and frameworks, AWS (SageMaker, Lambda, etc.), containerization, infrastructure as code (Terraform, CloudFormation)

Preferred Qualifications:

• Master's degree preferred.

• AWS or any other cloud provider certifications