Sr Machine Learning Engineer

aws, sagemaker, AI ML, Glue, pyspark, EMR, Lambda, machine learning, AWS- ML, ML pipeline architucture, AWS Machine Learning Specialty Certification, aws data analytics specialty certification
Contract W2, Contract Independent, Contract Corp-To-Corp, 12 Months
Depends on Experience

Job Description

Sr Machine Learning Engineer

Remote work

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.

Requirements

  • 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
Dice Id : 10371576
Position Id : 6898264
Originally Posted : 3 months ago
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