Position Overview: We are in search of a talented MLOps Engineer to play a vital role within our innovative Techunting AI team. As we strive to advance in the field of artificial intelligence, we acknowledge the essential function that MLOps serves in the effective deployment, management, and enhancement of machine learning models.

Key Responsibilities:

• Design and establish infrastructure for the deployment and management of ML models, primarily utilizing AWS services. This includes selecting orchestration tools to automate the ML workflow.
• Containerize models to guarantee consistency and facilitate portable deployment across various environments.
• Implement monitoring and tracking systems to oversee the health of ML models in production.
• Automate the transition of ML models from development to production.
• Manage version control for models and datasets.
• Collaborate with data scientists, AI engineers, and data engineers to comprehend the models and their requirements.
• Document ML workflows, encompassing deployment procedures, monitoring practices, and retraining strategies.
• Enforce security measures to safeguard sensitive information utilized in ML models and during deployment.
• Ensure compliance with data privacy regulations throughout the ML lifecycle.
• Develop monitoring dashboards to visualize model performance and proactively identify potential issues.

Qualifications:

• Bachelor's or Master's degree in Computer Science.
• A minimum of 5 years of experience in DevOps and MLOps.
• Strong grasp of machine learning concepts, algorithms, and techniques.
• Proficiency in machine learning libraries/frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience in model development, training, evaluation, and optimization.
• Ability to convert machine learning models into production-ready code.
• In-depth knowledge of AWS services pertinent to machine learning, including Amazon SageMaker, AWS Lambda, AWS Glue, AWS Step Functions, AWS Batch, and Amazon EMR.
• Familiarity with AWS storage and database services like Amazon S3 and Amazon RDS.
• Expertise in containerization technologies such as Docker and container orchestration with Kubernetes.
• Proficiency in managing infrastructure as code using tools like AWS CloudFormation or Terraform.
• Experience with continuous integration and continuous deployment (CI/CD) pipelines for machine learning models.
• Capability to monitor and troubleshoot production machine learning systems, ensuring high availability, scalability, and performance.
• Understanding of DevOps principles and practices, including automation, version control, and collaboration.
• Excellent communication, collaboration, and problem-solving skills.
• AWS certifications relevant to machine learning and operations, such as AWS Certified Machine Learning – Specialty, AWS Certified DevOps Engineer Professional, or AWS Certified Solutions Architect – Professional, would be highly advantageous.