Senior Machine Learning Engineer

What You'll Do

• Design, build, and implement complex systems for training and running machine learning models with sound architectural principles.
  
  
• Define work for other engineers and mentor them through completion.
  
  
• Understand business objectives and implement models to achieve those objectives along with metrics to track their progress.
  
  
• Demonstrate effective, respectful, and honest communication when collaborating with colleagues beyond the immediate team, including Product, Customer Success, and Sales/Marketing
• Close collaboration with data scientists in deploying, retraining and maintaining models that the data scientists have built.
  
  
• Apply development and testing best practices and demonstrate excellent software craftsmanship to produce maintainable, scalable, and quality solutions.
  
  
• Manage work through all phases of product development and delivery from Analysis & Design all the way through to successful Deployment.
  
  
• Deliver on company initiatives and projects prioritized for your team and support long term technical vision.
  
  
• Participate in peer design and code reviews.
  
  
• Show determination and discipline to drive forward improvements and innovations that add value and move the IT organization forward.
  
  
• Design & Build microservices-based data applications in Java and Python, with clear APIs and containerized deployments
• Implement Apache Beam transforms in Java and Python to process terrabytes of data with low latency

What Experience You Need

• BS degree in a STEM major or equivalent job experience required; Master's Degree preferred; AI/ML coursework preferred
• 5-7 years of related experience
• Proven track record of designing, developing, and deploying complex ML models in real-world applications
• Experience with model performance evaluation and ML model optimization for accuracy, efficiency and scalability
• Cloud Certification Strongly Preferred
• Experience in Python, Python Libraries - NumPy, Pandas, Scikit-learn
• ML Frameworks - Tensorflow, PyTorch, Keras, XGBoost etc
• Handling large scale Big Data (Structured and unstructured): Apache, Apache Beam, Datawarehouse - BQ Analys
• ML Model Development - Supervised, Unsupervised, Reinforcement learning.
  
  
• MLOps Deployment - Docker, Kubernetes
• Model Servicng - TensorFLow Serving, TorchServe