DMI

Machine Learning Engineer

Posted Date 2 weeks ago(7/28/2020 12:03 PM)
Job ID
2020-19519
Category
Analytics/ Data Analysis
Location
IN-KA-Bangalore

About DMI

DMI (Digital Management, LLC.), the world’s first end-to-end mobility company, combines all the skills and services necessary to deliver mobile enterprise solutions. Built to reinvent business through mobility, DMI has expertise in mobile strategy, UX, web, and app development, omni-channel commerce, brand and marketing, IoT and big data analytics, and secure device and app management. The company’s unique, integrated approach to mobility has resulted in dramatic growth as well as an expanding client base, which includes hundreds of Fortune 1000 commercial clients and all fifteen U.S. Federal Departments. DMI is headquartered in Bethesda, MD, with satellite offices around the world. The company was named one of the 2018 Top Workplaces in the Washington, DC area by The Washington Post and received Inc. Magazine’s Hire Power Award as one of the top 100 Private Job Creators in the US. Additional information is available at www.dminc.com and on LinkedIn, Twitter, Facebook, and Instagram.

About the Opportunity

We are looking for a passionate Machine Learning Engineer to work closely with our Data Scientists and Data Engineers to integrate and deploy models into our application’s production environment.

  

If you have a sense of adventure, take pride in solving complex data problems, and strive to build the best products on the planet, we want you on our Big Data Engineering team.

 

 

This role would be responsible for:

  • Providing solutions for the deployment, execution, validation, monitoring, and improvement of data science solutions.
  • Creating high performance and scalable Machine Learning systems
  • Building reusable production data pipelines for machine learning models
  • Collaborating with Data Engineers and Data Scientist to build data and model pipelines and help in running machine learning tests and experiments
  • Helping to manage the infrastructure and data pipelines needed to bring an ML solution to production
  • Troubleshoots production machine learning model issues, including recommendations for retrain, revalidate, and improvements

 

 

We are seeking people who are passionate about:

  • Working with data in all forms (structured, unstructured, video, audio, etc) and using it creatively to help solve problems
  • Collaborating with data scientists and data engineers to make a big impact
  • Staying in-tune with the big data community and encouraging the organization to utilize cutting edge technologies to innovate and invent solutions.

 

Qualifications

Technologies in our environment:

Skills - Experience and Requirements

You would be considered a great fit for this role if you have the following:

  • Bachelor’s degree in Computer Science Engineering, Data Science, or a related technical degree.
  • Experience in developing and deploying machine learning systems into production
  • Proven ability to learn things quickly and stay up to date on breakthroughs in Machine Learning techniques
  • Ability to quickly prototype new approaches and productionize solutions at scale for millions of active users
  • Ability to work in a Linux environment
  • Strong experience with Python, Scala, Golang, or Java
  • Experience with big data tools and processing technologies such as Apache Spark, Apache Flink, and cloud platforms like GCP or AWS
  • Experience in building containerized solutions using Kubernetes
  • You have worked with automated build systems such as Jenkins. A desire to write tools and applications to automate work
  • Experience implementing Continuous Integration or Continuous Delivery processes in engineering teams

These qualifications would make you stand out among other applicants:

  • Great communication skills - someone who is passionate about evangelizing the value of advanced data science capabilities.
  • Experience deploying and maintaining model Microservices
  • Experience building maintainable data pipelines for deep learning models
  • Familiarity with data-oriented workflow orchestration frameworks such as Kubeflow, Airflow

 

 

 

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