Sr Machine Learning Engineer
Skill: AWS & ML (SageMaker, EMR, GLue PySpark, Lambda,Kinesis)
The role will require excellent data science, machine learning engineering, and AWS architecture skills, as well as a high level of verbal and written
Duties & Responsibilities
- Self-driven member of the data science team using the Agile Scrum process to successfully deliver machine learning solutions which will delight customers, and/or internal stakeholders.
- Write high quality code in Python to deliver critical machine learning functionality.
- Work collaboratively with machine learning engineers, data engineers, DBAs, and business stakeholders.
- Passionately engage in an automation culture throughout delivery: testing, deployment, configuration.
- Participate in user story estimation and grooming sessions, driving to delivery predictability and reliability.
- Cooperate with data scientists and the lead machine learning engineer to ensure that your work follows
- Participate in regular peer code reviews with other team members as well as full-team development showcases.
- Experience with Python, object-oriented programming, and test-driven development
- Experience using Jupyter notebooks and common data science libraries, such as NumPy, Pandas, and Matplotlib
- Experience using TensorFlow, PyTorch, and/or MXNet
- Experience using Amazon SageMaker
- Experience developing end-to-end machine learning workflows and implementing machine learning engineering best practices
- Experience identifying machine learning use cases based on domain knowledge of the business operations/processes
- Experience with feature selection and dataset scoping for individual machine learning use cases by using SQL to query large relational databases, such as SQL Server, PostgreSQL, or AWS Aurora
- Experience with data preprocessing and feature engineering of structured datasets
- Experience using Amazon SageMaker’s built-in algorithms for a variety of use cases, such as linear regression, logistic regression, dimensionality reduction, anomaly detection, clustering, forecasting, and natural language processing
- Strong understanding how learning rate, batch size, L1/L2 regularization, network size, and other hyperparameters affect loss functions and model performance
- Experience defining, selecting and optimizing proper model accuracy metrics, such as precision, recall, F1 score, and AUC
- Experience with software engineering best practices, such as modularization, optimization, unit testing, proper documentation, logging, code commits/reviews, and clean code best practices
- Experience with Agile Scrum methodology
- Experience using Docker and ECR to containerize machine learning models as SageMaker estimators
- Experience deploying machine learning models to production as serverless microservices using AWS API Gateway and AWS Lambda
- Experience with real-time/batch inference and online/offline inference
- Experience with SOA design patterns, microservices architectures, and REST web services
- Demonstrate a portfolio of end-to-end machine learning solutions
- Desire to participate in building a DevOps/MLOps culture with commitment and ownership
- Excellent written, verbal and interpersonal communication skills
Other Desirable Skills/Experience
- Experience with AWS cloud-based infrastructure and services
- Experience building serverless architectures using AWS Lambda
- Experience with AWS AI/ML services, such as Rekognition, Transcribe, Polly, Lex, Translate, and/or Comprehend
- Experience with PySpark/EMR and distributed data preprocessing
- Experience with AWS Glue for serverless ETL
- Experience with Amazon Kinesis Firehose
- Experience with machine learning container orchestration, such as TFX/Kubeflow or ECS/EKS with Fargate
- Self-sufficient and highly motivated, self-directed and possess high energy
- Experience working in a virtual delivery environment