Req Number
R2937
Employment Type
Full time
Worksite Flexibility
Hybrid
Who We Are
CAI is a global technology services firm with over 8,500 associates worldwide and a yearly revenue of $1 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is rightwhatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.
Job Summary
We are looking for a MLOps Engineer to collaborate with data scientists, data engineers, and business stakeholders to ensure efficient, scalable, and reliable ML model deployment and monitoring. The role involves integrating ML models into production systems, automating workflows, and maintaining robust CI/CD pipelines. This position will be full-time and Hybrid.
Job Description
What You'll Do
- Model Deployment and Operationalization: Implement, manage, and optimize the deployment of machine learning models into production environments.
- CI/CD Pipelines: Develop and maintain continuous integration and continuous deployment pipelines to streamline the deployment process of ML models.
- Infrastructure Management: Design and manage scalable, reliable, and secure cloud infrastructure for ML workloads using platforms like AWS and Azure.
- Monitoring and Logging: Implement monitoring, logging, and alerting mechanisms to ensure the performance and reliability of deployed models.
- Automation: Automate ML workflows, including data preprocessing, model training, validation, and deployment using tools like Kubeflow, MLflow, and Airflow.
- Collaboration: Work closely with data scientists, data engineers, and business stakeholders to understand requirements and deliver solutions.
- Security and Compliance: Ensure that ML models and data workflows comply with security, privacy, and regulatory requirements.
- Performance Optimization: Optimize the performance of ML models and the underlying infrastructure for speed and cost-efficiency.
Required
What You'll Need
- Years of Experience: 4-6 years of experience in ML model deployment and operationalization.
- Technical Expertise: Proficiency in Python, Azure ML, AWS Sagemaker, and other ML tools and frameworks.
- Cloud Platforms: Extensive experience with cloud platforms such as AWS and Azure Cloud Platform.
- Model Development and Deployment: Expertise in developing, deploying, and maintaining machine learning models in production environments.
- Data Handling: Strong knowledge of data extraction, cleansing, preparation, and integration from various sources.
- CI/CD and Automation: Proficient in setting up and managing CI/CD pipelines and automating ML workflows.
- Collaboration: Proven ability to work collaboratively with cross-functional teams and stakeholders.
- Educational Qualification: Master's degree (preferably in Computer Science) or B.Tech / B.E.
Preferred
- Statistical Tools: Proficient in statistical tools and libraries such as Python, Azure Machine Learning, and Auto ML tools.
- Analytical Projects: Experience in end-to-end analytical project implementation on cloud platforms.
- Innovative Thinking: Ability to work independently with minimal supervision, demonstrating accountability, high work quality, and innovative thinking.
- Tool/Technique Knowledge: Up-to-date knowledge of new tools and techniques in the analytical field.
- Domain Knowledge: Familiarity with EMEA business operations is a plus.
Physical Demands
- Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc.
- Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor.
Reasonable accommodation statement
If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to [Confidential Information] or (888) 824 8111.
