Machine Learning Engineer

Are you ready to be part of a company that's not just talking about the future, but actively shaping it? Join The AES Corporation (NYSE: AES), a Fortune 500 company that's leading the charge in the global energy revolution. With operations spanning 14 countries, AES is committed to shaping a future through innovation and collaboration. Our dedication to innovation has earned us recognition as one of the Top Ten Best Workplaces for Innovators by Fast Company in 2022. And with our certification as a Great Place to Work, you can be confident that you're joining a company that values its people just as much as its groundbreaking ideas.

AES is proudly ranked #1 globally in renewable energy sales to corporations, and with $12.7B in revenues in 2023, we have the resources and expertise to make a significant impact as we provide electricity to 25 million customers worldwide. As the world moves towards a net-zero future, AES is committed to meeting the Paris Agreement's goals by 2050. Our innovative solutions, such as 24/7 carbon-free energy for data centers, are setting the pace for rapid, global decarbonization.

If you're ready to be part of a company that's not just adapting to change, but driving it, AES is the place for you. We're not just building a cleaner, more sustainable future - we're powering it. Apply now and energize your career with a true leader in the global energy transformation.

At AES, we have an amazing opportunity to transform the world with renewable energy. As one of the larger provides of renewable energy globally, we are dedicated to using the vast amounts of information available to design Smart Maintenance, Smart Operations, Smart Grid and other Energy related solutions to improve our operations and become the global leader in renewable energy generation. 

Machine Learning Engineers are the designers of self-running software that brings machines the ability to automate models that are predictive. They work with data scientists to take information and feed curated data into the models that they've uncovered or discovered. They use theoretical models within the data science sphere and build them out to scale as functioning and productive units or models that handle terabytes of real-time data.

Machine learning Engineers also function as a bridge or intersection for software engineering and data science. They use the available big data tools to improve programming frameworks and to collect raw data from pipelines. They redefine raw data into data science models that are ready to scale. Some machine learning engineers design the software programs that control technological tools, including computers or robots. They can develop algorithms that allow machines to identify trends or patterns in their programming data and as a self-contained unit, and a machine can then guide itself to understand commands, or even to think for itself. Machine learning engineers need a minimum of a bachelor’s degree in computer science or related fields.

Primary Duties and Responsibilities

• Communicate and reinforce the team's vision, purpose, and strategy.

• Collaborate with engineering leaders to transform research into AI capabilities within the platform.

• Develop text, image, and video analysis solutions for agents to enhance their business.

• Serve as a tech lead on a team of applied and data scientists.

• Drive ML projects from conception to completion, collaborating with data scientists, engineers, product teams, and other key partners.

• Design, develop, validate, deploy, and handle new functionalities, such as cash flow forecasting solutions.

• Partner with QA teams on test automation for new and existing functionalities.

• Conduct system integration and tests with other engineers.

• Monitor and troubleshoot performance issues in enterprise data pipelines.

• Work cross-functionally to identify business problems, design technical solutions, and deliver business impact.

• Ensure products are production-ready and function smoothly upon deployment.

• Launch new products and features, test their performance, and iterate quickly.

• Engage in cutting-edge research and development projects with a small team.

• Mentor and guide engineering teams to enhance technical expertise.

• Define database structures, identify data types for collection, and set up data analysis software.

• Research, modify, and apply data science and data analytics prototypes.

• Develop and construct methods and plans for machine learning.

• Use test findings for