Machine Learning Operations Engineer

Job Description Steer the Marketing Ship with Data-Driven Insights and Navigate Success Great marketing starts with great data and ends with groundbreaking results. Our client, Levelwing, a leading independent marketing agency , excels in transforming data into effective marketing strategies. For over twenty years, they have been pioneers in analytics and data-mining, shaping the future of marketing with their innovative approach. Their dedication to data-driven results ensures that brands achieve remarkable success. Data Scientists at Levelwing aid in reporting, strategy, and analysis of business ecosystems. They retain a high degree of curiosity and a passion for understanding complex datasets. They are proficient in data aggregation and synthesis to power actionable findings and recommendations. They approach their work with a positive “can do” mindset to meet a variety of business objectives, utilizing modeling and machine learning to push the boundaries of how Levelwing and our clients interact with datasets. Job Description: As a  Data Scientist  ( Machine Learning Operations Engineer) , you will be responsible for designing, deploying, and optimizing machine learning pipelines using AWS SageMaker and other AWS services. You will automate model training, tuning, and deployment processes while ensuring scalable and efficient infrastructure through CI/CD integration. Collaborating closely with data scientists, you will transition models from development to production, utilizing infrastructure-as-code tools like AWS CloudFormation. Your expertise in troubleshooting, monitoring, and optimizing resources will be critical in delivering impactful solutions that drive the success of our machine learning projects. Unfold Your Career Map: Employment Type: Full-time Shift: Night Shift; Mon - Fri 08:00 PM to 05:00 AM Work Setup: Permanent WFH Perks: Day 1 HMO, Above-market salary, Global exposure, Weekends Off Molding Data into Gold: Key Responsibilities and Daily Tasks Automating machine learning model training, tuning, and deployment using AWS SageMaker. Developing and maintaining end-to-end machine learning pipelines with CI/CD integration. Managing code versioning with Git and AWS CodeCommit to ensure scalable and maintainable Python code. Implementing infrastructure-as-code solutions using AWS CloudFormation for managing AWS resources. Setting up and monitoring SageMaker Endpoints for real-time and batch inference. Tracking model performance post-deployment through AWS CloudWatch and Model Monitor. Collaborating with data scientists and data professionals to transition models from development to production. Optimizing AWS resources to manage costs effectively. Troubleshooting and debugging machine learning pipelines to ensure smooth operations. Documenting pipeline architecture, workflows, and best practices for team collaboration. Scaling existing machine learning models and deploying new models at scale. Supporting the Analytics team with data modeling, regression analysis, and correlation analysis. Ensuring accuracy and attention to detail in all work. Managing, integrating, and transforming data for accurate and normalized analysis. Providing recommendations on business rules to enhance operational efficiency. Understanding business objectives, needs, and requirements comprehensively. Analyzing metrics creatively to improve processes and provide insights. Collaborating with teammates to present information in a clear and impactful manner. Requirements The Right Stuff: Skills and Experience to Break New Ground Bachelor’s degree in data science, engineering, mathematics, statistics, marketing , or a related field. Strong experience using AWS SageMaker for model training, tuning, and deployment. Proficiency in Python , including libraries such as TensorFlow, PyTorch, or Scikit-learn. Experience using CI/CD tools ( AWS CodePipeline, CodeBuild) for automated model deployment. Hands-on experience with infrastructure-as-code tools like AWS CloudFormation or Terraform. Expertise in version control using Git and AWS CodeCommit . Knowledge of containerization technologies such as Docker. Familiarity with AWS services, including S3, Lambda, EC2, and CloudWatch, for managing and optimizing resources. Strong skills in troubleshooting, debugging, and optimizing machine learning pipelines. Experience collaborating with data scientists to transition models into production. Knowledge of monitoring and logging tools such as AWS CloudWatch and SageMaker Model Monitor. Required Skills: Demonstrated ability to multi-task and manage multiple work streams simultaneously. Proven ability to learn new technology platforms in a timely manner. Strong attention to detail. Exceptional analytical skills. Benefits Bounty of Benefits: What’s in Store for You Day 1 HMO coverage with free dependent Competitive Package Remote/Permanent WFH arrangement Fixed weekends off Unlimited upskilling through Emapta Academy courses (Want to know more? Visit https://bit.ly/EmaptaTrainingCalendar ) Free 24/7 access to our office gyms (Ortigas and Makati) with a free physical fitness trainer! Exclusive Emapta Lifestyle perks (hotel and restaurant discounts, and more!) Unlimited opportunities for employee referral incentives across the organization Standard government and Emapta benefits Total of 20 annual leaves to be used on your own discretion (including 5 credits convertible to cash) Fun engagement activities for employees Mentorship and exposure to global leaders and teams Career growth opportunities Diverse and supportive work environment Welcome to Emapta Philippines! Discover a world of possibilities at Emapta, where your career takes flight in stability and growth. Join a team that thrives on camaraderie, supporting each other to achieve excellence together. Experience the satisfaction of being recognized for your contributions with competitive compensation packages that reflect your skills and commitment. Immerse yourself in a positive work culture that encourages collaboration, innovation, and personal development. We provide you with the platform for your success, empowering you to reach new heights in a supportive and inclusive environment. With a wide roster of international clients from various industries and a proven track record of success, Emapta offers a stable foundation for your career. Team up with like-minded professionals who are passionate about making a meaningful impact through premium global opportunities at your fingertips. Apply now and create a better future with us. #EmaptaExperience Requirements The Right Stuff: Skills and Experience to Break New Ground Bachelor’s degree in data science, engineering, mathematics, statistics, marketing, or a related field. Strong experience using AWS SageMaker for model training, tuning, and deployment. Proficiency in Python, including libraries such as TensorFlow, PyTorch, or Scikit-learn. Experience using CI/CD tools (AWS CodePipeline, CodeBuild) for automated model deployment. Hands-on experience with infrastructure-as-code tools like AWS CloudFormation or Terraform. Expertise in version control using Git and AWS CodeCommit. Knowledge of containerization technologies such as Docker. Familiarity with AWS services, including S3, Lambda, EC2, and CloudWatch, for managing and optimizing resources. Strong skills in troubleshooting, debugging, and optimizing machine learning pipelines. Experience collaborating with data scientists to transition models into production. Knowledge of monitoring and logging tools such as AWS CloudWatch and SageMaker Model Monitor. Required Skills: Demonstrated ability to multi-task and manage multiple work streams simultaneously. Proven ability to learn new technology platforms in a timely manner. Strong attention to detail. Exceptional analytical skills.