MLOps Engineer

Job Description About you You are someone who yearns to influence your own growth. You're seeking a company where you can explore your passions and enhance your professional development. You bring to Applaudo the following competencies: - An undergraduate degree or higher in computer science, computer engineering, or a related discipline. - More than 8 years working on software development projects, including deployment phases. - Over 4 years of practical experience in data science and machine learning. - Over 2 years of hands-on MLOps experience. - General knowledge of classic ML algorithms for tabular data such as decision trees, random forest, XGBoost, and model evaluation and optimization techniques. - Proficiency in programming languages like Python. Familiarity with Scikit Learn and TensorFlow would be a plus. - Experience with containerization and image optimization (Docker), orchestration (Kubernetes), and version control (e.g., Git). - Experience with cloud platforms like AWS, especially with Lambda Functions and ECS. - Understanding of data pipelines, ETL processes, and data storage technologies. - Proficiency in setting up CI/CD pipelines. - Experience with monitoring tools such as Prometheus and Grafana, ELK for logging. - Knowledge and experience with relational databases (SQL). - Understanding of security best practices for machine learning models and data. - Knowledge of setting up a Jupyter server for ML experimentation. - Experience with tools for versioning datasets. - Excellent communication skills to effectively collaborate with varied teams including data scientists, engineers, and stakeholders. - Ability to troubleshoot and solve complex issues related to model deployment and production systems. - Capability to work in a team-oriented, agile environment, contributing to the team and organizational success. - Advanced English proficiency is required, as you will be interacting directly with US-based clients. Your responsibilities will include: - Checking deployment pipelines for machine learning models, ensuring they are reliable, scalable, and well-documented. - Reviewing code changes and pull requests from the data science team. - Triggering CI/CD pipelines post code approvals. - Monitoring pipelines and ensuring all tests pass and model artifacts are correctly generated/stored. - Deploying updated models to production after pipeline completion. - Consciously collaborating with the software engineering and DevOps team to ensure seamless integration. - Containing models using Docker and deployment on cloud platforms. - Setting up monitoring tools to track metrics like response time, error rates, and resource usage. - Establishing alerts and notifications for early anomaly or deviation detection. - Analyzing monitoring data, log files, and system metrics. - Developing and maintaining automated pipelines for model training, testing, deployment, and monitoring. - Setting up and managing requisite cloud infrastructure and resources for ML model deployment. - Collaborating with the data science team to develop pipelines covering any faults, and creating and maintaining data pipelines for model inputs and outputs. - Optimizing ML systems for scalability, ensuring models perform effectively under varied loads. - Implementing security measures to protect sensitive data and machine learning models from unauthorized access. - Documenting and troubleshooting changes and optimizations. Additional Information All your data will be kept confidential as per EEO guidelines. At Applaudo Studios we value trust, communication, respect, excellence, and teamwork. We understand we are working with top-tier talent and thus treat each other with mutual respect and admiration. Submit your application today, don't miss this chance to join the best digital team in the region! At Applaudo Studios, we genuinely appreciate all the hard and excellent work our team does daily. Hence, we consciously design our perks as a token of gratitude for their dedication and brilliance. Some of our perks and benefits: - Remote working opportunity - Flexible scheduling - Celebrations - Special discounts - Recreation space - Flexible workspaces - A positive work environment - Private health insurance *Benefits may differ based on your location and/or availability. Request more information when applying.