Machine Learning Ops Engineer

As an ML Ops Engineer, you will be at the forefront of technology, bridging the gap between data science and production environments. Your role involves designing, implementing, and maintaining end-to-end machine learning pipelines that enable seamless deployment and management of ML models in production. Additionally, you'll play a crucial part in mentoring and building a team of junior ML Ops Engineers.What You’ll Do: Collaborate with data science teams to create a streamlined, automated pipeline for transitioning ML models from development to production.Design, Develop and maintain processes for model versioning, training, deployment, and continuous updates.Implement best practices for model monitoring, performance evaluation, and drift detection.Design and build scalable, reproducible infrastructure for ML development and deployment.Implement infrastructure as code (IaC) principles to ensure consistent and reliable ML environments.Integrate ML pipelines into continuous integration and continuous deployment (CI/CD) workflows.Ensure reliable and efficient deployment of ML models into production environments.Implement monitoring solutions to track model performance, data quality, and system health.Troubleshoot and optimize ML pipelines for improved efficiency, reliability, and scalability.Implement robust security controls and access management for ML systems.Ensure compliance with industry standards (e.g., GDPR, HIPAA) during model deployment.Collaborate with legal and compliance teams to address regulatory requirements.Lead and mentor a team of junior MLOps Engineers, fostering their growth and skill development.Provide technical guidance on MLOps frameworks, deployment strategies, and observability best practices.Cultivate a culture of continuous learning, innovation, and knowledge sharing within the team.Work closely with business stakeholders to understand ML project objectives and requirements.Communicate effectively with project managers and cross-functional teams.Provide regular updates on project progress, performance, and any issues or risks. What You’ll Bring:A relevant Bachelors or higher-education degree in Computer Science, Data Science, or related fields. 3 to 5 years of hands-on experience in cloud engineering, infrastructure, or related roles.Minimum of 4 years of professional experience in MLOps, machine learning, and DevOps.Preferred certifications in cloud platforms (e.g., AWS, Azure, GCP) and MLOps.Proficiency in Python, SQL, and relevant ML libraries (e.g., TensorFlow, PyTorch).Familiarity with cloud platforms (e.g., GCP, AWS) and containerization (Docker, Kubernetes).Strong problem-solving skills and ability to work in a collaborative environment.Expertise in software development methodologies, such as Agile, DevOps, and CI/CD.Proficiency in programming languages, including Python and R.Ability to travel to other client offices or locations as needed.Strong communications skills.Offer:Contract of EmploymentPossibility to grow in new team of professionalsWork on impactful, real-world projects that challenge your skills and contribute to cutting-edge solutionsProfessional development through training, mentorship, and career advancement paths