We are currently seeking an experienced Machine Learning Engineer to join our growing Data Science & Machine Learning Engineering Practice i...
We are currently seeking an experienced Machine Learning Engineer to join our growing Data Science & Machine Learning Engineering Practice in Chicago. In this role, you will collaborate on the entire lifecycle of infrastructure and custom machine learning solutions delivery. As a senior member of the practice, your experience and expertise will provide mentorship to other consultants and advisement across projects.
A little about you:
You are someone who likes to stay on the cutting edge of artificial intelligence, data science and software engineering through novel project execution and development of algorithms that improve performance and effectiveness across business domains. The role requires a broad knowledge of Software Development Life Cycle, Machine Learning Life Cycle and DevOps practices, and you like the ability to apply creativity to invent and customize how these three disciplines join together to address specific client needs.
- 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.
What You'll Bring
- Bachelor's degree in Computer Science 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
- Professional experience with cloud technologies (Azure, AWS, GCP)
- Proficiency in PySpark, SparkSQL, Databricks Delta Lake and MLFlow
- Experience with common data science toolkits and model development
- Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture
- Strong working knowledge of AI-type algorithms, including machine learning techniques such as regression, decision trees, probability networks, association rules, clustering, neural networks, and Bayesian models.
- Experience working 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 with high velocity high volume streaming data (Kafka, Event Hubs, Stream Analytics) is a plus
- Experience with data transformation technologies (dbt) and feature engineering is a plus
- Experience with deep learning libraries, such as Tensorflow and PyTorch is a plus.
- Strong communication skills with the ability to explain complex topics to both a technical and non-technical audience
- Strong team player skills with the ability to work effectively in a collaborative, fast-paced, multi-tasking environment.