Machine Learning & Operations Lead
Overview EXL (NASDAQ: EXLS) is a leading operations management and analytics company that designs and enables agile, customer-centric operating models to help clients improve their revenue growth and profitability.
Our delivery model provides market-leading business outcomes using EXL’s proprietary Business EXLerator Framework™, cutting-edge analytics, digital transformation and domain expertise. At EXL, we look deeper to help companies improve global operations, enhance data-driven insights, increase customer satisfaction, and manage risk and compliance.
EXL serves the insurance, healthcare, banking and financial services, utilities, travel, transportation and logistics industries. Headquartered in New York, New York, EXL has more than 32,000 professionals in locations throughout the United States, Europe, Asia (primarily India and Philippines), South America, Australia and South Africa. For more information, visit www.exlservice.com.
Machine Learning & Operations Lead
Roles and Responsibilities:
• A key stakeholder for Machine learning Operations and own responsibility to operationalize and monitor machine learning models using high-end tools and technologies.
• Collaborate with data scientists, data engineers, and other key stakeholders to solve complex problems and create unique solutions for MLOps.
• Solid understanding of machine learning fundamentals and familiarity with Machine Learning Development Lifecycle.
• Bring deep expertise in cloud architecture / DevOps to analyze and recommend enterprise-grade solutions for operationalizing AI / ML analytics.
• Develop end-to-end (Data/Dev/ML)Ops pipelines based on an in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably.
• Designs and builds pipelines that shorten development cycles for our software and AI/ML systems:
• Build and automate our AI/ML work stream from data analysis, experimentation, operationalization, model training, model inference, model evaluation, and model tuning to visualization (includes versioning, compliance, and validation).
• Improve and maintain our automated CI/CD pipeline.
• Continuously evaluate the latest packages and frameworks in the ML ecosystem.
• Execute best practices in version control and continuous integration/delivery.
• Work well in a fast-paced cross-functional environment.
• >5 years of experience in Data Science / ML / AI and operationalization of models.
• At least 3 years of experience in Python
• At least 2 years of experience in AWS services Sagemaker, EC2, S3, EMR, Lambda Functions, Cloudwatch, etc.
• At least 2 years of experience in orchestrating ML Jobs on Airflow, Step Functions, etc.
• At least 2 years of experience in Production like environment.
• Good to have knowledge in Bigdata Technologies specifically on HDFS, Spark, and Hive.
• Good understanding of AI engineering and MLOps concepts.
• Experience with Machine learning frameworks, libraries, and agile environments.
• Experience in deploying machine learning solutions using DevOps principles.
• Experience in implementing end-to-end machine learning life cycle on cloud preferably in AWS.
• Experience with version control tools such as Git etc.
• Experience with SQL and databases.
• Nice Programming skills.
• Knowledge in one or more of Docker, Jenkins, Kubernetes, and other DevOps tools.
• Familiarity with Kubeflow or MLflow would be plus.
• Outstanding analytical and problem-solving skills.
- Pay Type Salary
- Travel Required No
- Required Education Bachelor’s Degree
- Stamford, CT, USA