MLOps Engineer

Job Description:

Fractal is a leading AI & analytics organization. We have a strong Full stack Team with great leaders accelerating the growth. Our people enjoy a collaborative work environment, exceptional training, and career development as well as unlimited growth opportunities. We have a Glassdoor rating of 4/5 and achieve customer NPS of 9/10. If you like working with a curious, supportive, high-performing team, Fractal is the place for you.

Responsibilities:

• Experience in React and Java, you would be part of the team consisting of Scrum Master, Cloud Engineers, AI/ML Engineers, and UI/UX Engineers to build end-to-end Data to Decision Systems.
• Demonstrable experience designing, and building enterprise web applications
• Expert-level proficiency with JavaScript (ES6), HTML5 & CSS
• Expert-level proficiency with Java, Spring Boot or Play Framework
• Architecting, estimating, managing, developing & maintaining the backend and frontend for various Data to Decision projects for our Fortune 500 client
• Work closely with the data science & engineering team to integrate the algorithmic output from the backend REST APIs
• Work closely with business and product owners to create dynamic infographics with intuitive user controls
• Help the junior engineers on the project to effectively code and deliver as per the sprint plan and take care of their career aspirations and goals
• Be the speaker in translating technical project journey and sharing with clients
• Participate in UAT, and diagnose & troubleshoot, bugs and application integration issues
• Create and maintain documentation related to the developed processes and applications
• Model Deployment: Design, deploy, and manage machine learning models using AWS SageMaker, ensuring production readiness and scalability.
• Containerization and Orchestration: Utilize Docker for containerization and Kubernetes for orchestration to manage and scale ML workloads efficiently.
• Platform Management: Use OpenShift or Amazon EKS to manage and scale ML infrastructure, ensuring high availability and reliability.
• Production Scalability: Implement strategies for scaling ML models in production environments to handle increased loads and ensure low-latency predictions.
• ML Gateway Management: Set up and manage ML gateways to streamline model serving, enhancing security and performance.
• Monitoring and Logging: Implement monitoring solutions using Datadog to track model performance, detect anomalies, and ensure system health.
• CI/CD Pipelines: Develop and maintain CI/CD pipelines for ML models to automate testing, deployment, and updates, ensuring rapid and reliable delivery.
• Continuous Improvement: Stay updated with the latest advancements in MLOps, cloud-native technologies, and AWS SageMaker features to improve ML infrastructure.
  Qualifications:
• Technical Expertise: Proven experience with AWS SageMaker, Kubernetes, Docker, OpenShift, and Datadog.
• MLOps Knowledge: Strong understanding of MLOps practices, including model deployment, monitoring, and lifecycle management.
• Cloud-Native Skills: Proficiency in cloud-native technologies and infrastructure management.
• CI/CD Experience: Hands-on experience with CI/CD tools and practices, such as Jenkins, GitLab CI, or AWS CodePipeline.
• Monitoring Tools: Familiarity with monitoring and logging tools like Datadog for real-time performance tracking.
• Problem-Solving: Excellent problem-solving skills and the ability to troubleshoot complex ML systems.
• Collaboration: Strong collaboration and communication skills to work effectively with cross-functional info security and compliance teams.

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