Job Responsibilities
- Develop and operationalize scalable processes to effectively manage large and complex client engagements.
- Ensure profitable delivery and exceptional customer experience through the design, deployment, operation, and maintenance of scalable and efficient automated machine learning systems.
- Monitor and enhance system performance, rigorously addressing information security, access controls, model governance, and data/model drift.
Job Requirements
- Proficiency in designing and implementing cloud solutions (AWS, MS Azure, or GCP) and building MLOps pipelines on these platforms.
- Experience with MLOps frameworks such as Kubeflow, MLFlow, DataRobot, and Airflow, along with expertise in Docker, Kubernetes, and OpenShift.
- Strong programming skills in languages like Python, Go, Ruby, or Bash, with a solid understanding of Linux.
- Knowledge of machine learning frameworks including scikit-learn, Keras, PyTorch, and Tensorflow.
- Ability to comprehend tools utilized by data scientists and experience in software development and test automation.
- A good grasp of advanced AI/ML algorithms and their practical applications.
- Familiarity with Applications and Digital Automation Solutions, Low Code/No Code platforms, API design, and exposure to DevOps, React/Angular, containers, and building visualization layers.
- Knowledge of self-service analytics platforms like Dataiku, KNIME, or Alteryx is considered an advantage.
- Proficiency in MS Excel is mandatory.
- Familiarity with the Life Sciences industry is a plus.
