ML Engineer

• Deploy machine learning workloads, preferably in AWS using SageMaker.
• Implement MLOps practices including monitoring, alerting, CI/CD pipelines, Docker containerization, and testing.
• Work closely with the data engineering and software teams to ensure seamless deployment of ML models into production environments.
• Ensure code quality and maintain version control using GitHub, following best practices and collaborative workflows.
• Leverage cloud-native services on AWS (S3, API Gateway, Lambda, ECS) or comparable platforms like MS Azure and Google Cloud.
• 06Collaborate with cross-functional teams to translate business requirements into actionable technical solutions.

• Advanced proficiency in Python and strong knowledge of SQL.
• Hands-on experience deploying machine learning workloads in cloud environments, preferably AWS SageMaker.
• Practical experience implementing MLOps, including monitoring, alerting, CI/CD pipelines, Docker builds, and automated testing.

Nice to Have

• Proficiency with version control systems like GitHub, including familiarity with workflows and collaborative coding practices.
• Experience working with AWS cloud-native services such as S3, API Gateway, Lambda, ECS or equivalent services on MS Azure or Google Cloud.
• Strong communication skills in English, both written and verbal.

At IT Solutions Hub, we are committed to delivering innovative and cutting-edge

technology solutions to our clients. We focus on leveraging AI and Machine

Learning to build intelligent systems that drive efficiency and create data-driven

insights. Join our dynamic team of professionals where your contributions directly

impact businesses on their digital transformation journey.