Machine Learning Engineer
The work you will do:
- Become a part of an accomplished team dedicated to crafting ML solutions for intricate healthcare challenges.
- Work closely with data scientists, engineers, and technical teams to enhance the delivery of ML systems.
- Monitor model performance, suggest remedies, and ensure comprehensive code coverage.
- Stay informed about the latest ML advancements, techniques, open-source tools, and cloud offerings.
- Play a role in complete AI/ML projects, steering successful business outcomes and transitioning models to production.
- Lead the design and implementation of the MLOps framework, while spearheading pipeline construction.
- Utilize contemporary software engineering methods to create and implement scalable AI/ML systems.
- Customize API integrations to connect cloud-based systems seamlessly.
- Engage in discussions about architecting compliant and high-performance ML platforms.
- Champion MLOps proficiency, providing education and training to technical teams.
The skills and qualifications you need:
- Bachelor's degree in fields including Computer Science, Data Analytics, Software/Computer Engineering, Computational Statistics, Mathematics, or related disciplines.
- 2+ years of MLOps experience, concentrating on deploying, monitoring, detecting drift, re-training, and roll-back of ML solutions.
- 3+ years of experience in machine learning, crafting models to achieve desired outcomes and producing code for production.
- 5+ years of experience with Database Management Systems, Data Lakes, and cloud-based ML Ecosystems such as Spark or Databricks.
- Excel in preventing ML-system anti-patterns and reducing dependencies during design and execution.
- Display strong abilities in critical thinking and analysis.
- Exhibit a passion for pioneering ML solutions within a business environment.
- Possess expertise in constructing resilient systems for active learning and continuous training in production.
- Demonstrate a proven skillset in scripting and database languages like Python, SQL, and Scala.
- Assume hands-on roles in the deployment of machine learning and deep learning solutions, utilizing contemporary frameworks like Spark, MXNet, Tensorflow, Keras, and PyTorch.