Job Title: MLOps Engineer Location: Remote (Brazil) Duration: 6-12 months (with possible extension or FTE conversion) We are seeking a talented and experienced MLOps Engineer to join our dynamic team. This role is pivotal in bridging the gap between machine learning and operations, ensuring efficient and scalable deployment of AI models and solutions. If you have a strong background in DevOps, Machine Learning, and AWS, and are passionate about integrating cutting-edge technologies in a collaborative environment, we would love to hear from you. Experience in the life sciences sector is a plus. Key Responsibilities: - Design, implement, and maintain robust MLOps pipelines to streamline the deployment of machine learning models in production environments. - Collaborate with data scientists and software engineers to integrate machine learning models into existing systems. - Develop and manage ETL processes to ensure data is clean, reliable, and accessible for machine learning tasks. - Implement and manage CI/CD pipelines for machine learning models and applications. - Monitor and optimize the performance of deployed models, ensuring they meet business requirements and performance benchmarks. - Work closely with cross-functional teams to understand business needs and provide technical solutions. - Ensure compliance with best practices in data security and privacy, particularly in regulated industries like life sciences. - Stay updated with the latest trends and technologies in MLOps, Gen AI, and DevOps, and advocate for their adoption where beneficial. Required Qualifications: - Bachelor's degree in Computer Science, Engineering, or a related field. - 3-5 years of experience in DevOps with a focus on MLOps. - Proven experience in deploying and managing machine learning models in production. - Strong understanding of AWS services and cloud-based ML solutions. - Excellent communication skills, with the ability to work effectively in a remote team setting. - Experience working in or with the life sciences industry is a plus. Desired Skills and Knowledge: - Familiarity with Generative AI and its applications. - Experience with ETL tools and processes. - Knowledge of containerization technologies such as Docker and orchestration tools like Kubernetes. - Proficiency in programming languages such as Python, R, or Java. - Understanding of data privacy regulations and compliance requirements in life sciences. Tools and Technologies: - AWS (SageMaker, Lambda, EC2, S3, etc.) - Docker and Kubernetes - Jenkins or other CI/CD tools - ETL tools like Apache Airflow or Talend - Machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn - Version control systems like Git. Skills: MLOps, GenAI, DevOps, ETL, Machine Learning, AWS.