Why Blue Coding?
At Blue Coding, we specialize in hiring excellent developers and amazing people from all over Latin America and other parts of the world. For the past 11 years, we’ve helped cutting-edge companies in the United States and Canada build great development teams and develop great products. Large multinationals, digital agencies, Saas providers, and software consulting firms are just a few of our clients. Our team of over 150 engineers, project managers, QA, UX/UI designers, and many more is distributed in more than 10 countries across the Americas. We are a fully remote company working with a wide array of technologies, and we have expertise in every stage of the software development process.
Our team is highly connected, united, and culturally diverse, and our collaborators are involved in many initiatives around the world, from wildlife preservation to volunteering at local charities. We stand for honesty, fairness, respect, efficiency, hard work, and cooperation.
What are we looking for?
In this opportunity, we are looking for an experienced DevOps Engineer with experience integrating Machine Learning models to work with one of our US clients, a corporation that, through its subsidiaries, provides life insurance protection targeted to the middle American market. The ideal candidate will join our dynamic team and help us build and maintain robust machine-learning pipelines. This role requires a strong knowledge of DevOps practices, AWS, Terraform and the skills to migrate, integrate, and optimize those systems.
If you are independent, a great communicator, with great attention to detail, and a problem solver this is a great fit for you! Our jobs are fully remote and flexible - as long as you have the skills and can get the work done well, you can work anywhere in the listed countries (and anytime) you want.
Here are some of the exciting day-to-day challenges you will face in this role:
- Collaborate with data scientists, ML engineers, and data engineers to design and implement scalable and reliable ML pipelines and real-time inference services.
- Automate the end-to-end ML lifecycle, including data ingestion, model training, evaluation, deployment, and monitoring.
- Develop and maintain CI/CD pipelines for ML models to ensure seamless integration and deployment of ML models.
- Monitor and optimize the performance of deployed models, ensuring they meet business requirements and performance standards.
- Assist with setup and configuration of monitoring and optimization systems for deployed models, ensuring they meet business requirements and performance standards.
- Troubleshoot and resolve issues related to ML model deployment.
- Stay up to date with the latest advancements in relevant tools and technologies.
You will shine if you have:
- A bachelor’s degree in computer science, information systems, related field, or equivalent work experience.
- Strong experience in DevOps and MLOps with Terraform and CI/CD experience.
- Strong experience with AWS network & security architecture and containerization technologies.
- Experience working with Atlassian Jira, Confluence and Bitbucket pipelines.
- Experience in programming skills in languages such as Python, Go, Java, or Scala.
- Excellent problem-solving skills and attention to detail.
- Excellent communication and interpersonal skills, with the ability to collaborate effectively across different departments.
- Experience with big data technologies such as Hadoop and Spark
- Experience with ML frameworks & libraries (e.g., TensorFlow, XGBoost, scikit-learn/sklearn) and MLOps tools
- Self-motivated and able to work independently and in a team environment.
- Flexibility to adapt to changing priorities and handle multiple tasks simultaneously.
- Strong sense of personal responsibility and accountability delivering excellent work both at a team level and personal level
- Certification(s) in AWS Certified Cloud Practitioner and AWS Certified Machine Learning Engineer or AWS Certified DevOps Engineer strongly desired.
What we offer:
- Salary in USD
- Flexible schedule (within US Time zones)
- 100% Remote
Ready to learn more? Apply below!