Machine Learning Engineer

Responsibilities

• Collaborate with data science & engineering teams to deliver end to end machine learning solutions for our clients.
• Collaborate with platform teams and solution architects to evolve big data platforms and evaluate various data science technologies and services.
• Use your experience and communication skills to work across business & technology teams to build and develop innovative data science models and algorithms.
• Build end-to-end machine learning pipelines by developing and applying creative solutions that go beyond current tools and work alongside data scientists to build predictive models. • Build and maintain MLOps tools and platforms.
• Deliver production-grade, machine learning solutions, built with automated, repeatable processes.
• Design, build, test, deploy and monitor Machine Learning solutions in production.
• Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making.

Required Qualifications

• Preferred Bachelor’s degree in related STEM field or related field and relevant work experience
• Proven track record of building end to end machine learning pipelines and big data solutions with working experience of CI/CD, MLOps
• Experience collaborating with cross-functional teams including data engineers and data scientists to deliver end-to-end solutions
• Experience in production model management
• Knowledge of automated testing and monitoring solutions
• Experience with the software development life cycle and machine learning life cycle
• Experience as a software engineer writing production code is a big plus
• Deep technical proficiency in Python and SQL
• Proficiency in PySpark, SparkSQL, Delta Lake and MLFlow
• Professional experience with cloud technologies (Azure, AWS, GCP)
• Experience with common data science toolkits and model development
• Extensive knowledge of ML frameworks, libraries, data structures, data modeling, feature engineering, and software architecture
• Strong working knowledge of machine learning techniques, including linear and logarithmic regression, decision trees, probability networks, association rules, clustering, neural networks, and Bayesian models.
• Experience with big data platforms (Hadoop, Spark, Hive), orchestration frameworks (Airflow), Infrastructure technologies (Terraform, Ansible, Kubeflow, Kubernetes, Docker) ,data Science environments and libraries (Databricks, Sagemaker, Jupyter, Anaconda, Sklearn, MLLib, NumPy) and CI/CD build and version management tools (Jenkins, Git, DVC)
• Experience working with the following is a plus: high velocity high volume streaming data (Kafka, Event Hubs, Stream Analytics), data transformation technologies (dbt), and deep learning libraries, such as Tensorflow and PyTorch, and applications with NLP and computer vision