Engineer, Machine Learning

As a Engineer, Machine Learning you will work with our AI Solutions team to design, develop, build MLOps workflows and help deploy machine learning models to solve complex business problems. You will help our customers build modern data solutions on the AWS stack.

This position is 100% remote with up to 25% travel required.

Responsibilities

Under the supervision of Big Data Consultants and Architects, work with multiple clients simultaneously to implement enterprise-wide scalable operations on AWS Deploy and monitor machine learning models on AWS using tools such as SageMaker Implement machine learning pipelines including data cleaning, training, evaluation and deployment Develop models from customer data to meet customer goals Write infrastructure as code scripts in CDK or Terraform to help make service deployment more efficient and consistent Create data visualizations and reports from the data that has been extracted, transformed and loaded with tool such as Amazon Quicksight Collaborate with data scientists, data engineers, and product managers to document requirements and deliver machine learning solutions Develop and maintain data pipelines, feature engineering, and model training and deployment framework Conduct exploratory data analysis, data preprocessing, and data cleaning Perform model evaluation, selection, and optimization Implement and maintain automated testing and monitoring for machine learning models in production

Requirements

Design & implementation experience with distributed applications Experience in database architectures and data/MLOps pipeline development Ability to work with loading and extracting data from Glue, SQL, DDL, DML commands Working knowledge of AWS data technologies, like Sagemake MLFlow or Sagemaker MLOps experience Working knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or similar Python or R experience Working knowledge of AWS cloud computing platforms and services such as Sagemaker, S3, Athena,and Lambda Working knowledge of software engineering principles and best practices, including version control, testing, and continuous integration/continuous deployment (CI/CD) Ability to handle unstructured, semi-structured data, working in a data lake environment Ability to work in an Agile environment Working knowledge of software development tools and methodologies Presentation skills with a high degree of comfort speaking with IT management, customers, and developers AWS Certification (required within 6 months of hire)

Benefits & Compensation

Access to health, vision and dental insurance with options Generous Paid Time Off (FlexPTO, parental leave, volunteering time off) Reproductive health benefits Pet insurance 401k matching program Life insurance paid by Mission Cloud An internal department dedicated to helping team members on their career path Inclusive work environment with several Employee Resource Groups

Placement within the range is determined by a variety of factors, including but not limited to knowledge, skills, and ability as evaluated during the interview process. The compensation range for the base salary of this role is: $100,000 - $160,000.

Use of Artificial Intelligence (AI)

Our company leverages Artificial Intelligence (AI) as a tool to enhance and streamline various aspects of the hiring process. By submitting your application, you acknowledge and consent to the use of AI technologies in activities such as resume screening, interview scheduling, note taking and other administrative functions. Please note that all hiring decisions are made by human reviewers in compliance with applicable laws and best practices.

About Mission Cloud

Mission Cloud is an Amazon Web Services (AWS) Premier Consulting Partner and MSP. Clients depend on us to expertly and securely architect, migrate, manage, and optimize their cloud environments. Mission Cloud’s team of AWS Certified Solutions Architects and DevOps Engineers are ready to help you harness the full power of the AWS cloud to transform your business and operations.