AI / ML Engineer II (P2)

About the Role:
As a Machine Learning Engineer at this level, you will be a key contributor to our team, responsible for building and deploying end-to-end machine learning models. You will work with a degree of autonomy on well-defined projects, translating business needs into functional and scalable ML solutions. This role is perfect for hands-on ML Engineers looking to deepen their expertise and take on more complex challenges.

Independently build, train, and deploy machine learning models for complex projects.
Provision, configure, and manage core AWS infrastructure supporting ML training and deployment pipelines-utilizing VPC for networking, EC2 and EKS for compute and Kubernetes container orchestration, EMR for big data processing, S3 for artifact and data storage, and RDS for relational database management.
Design and maintain robust, end-to-end ML pipelines, from data processing to model serving.
Contribute to technical design discussions and provide input on system architecture and best practices.
Collaborate with business, product, and other engineering teams to understand requirements and translate them into technical specifications.
Write high-quality, production-ready code and participate in code reviews to maintain our standards of excellence.
Mentor interns or junior engineers, sharing your knowledge and expertise.
Qualifications

Basic Qualifications:
Bachelors or Masters degree in Engineering, Mathematics, Statistics, or a related field.
3-6 years of professional experience in machine learning engineering.
Proven experience in building and deploying ML models in a production environment.
Strong proficiency in Python, SQL, and experience with a distributed computing framework (e.g., Spark).
Practical experience managing and deploying ML workloads on AWS using VPC, EC2, EMR, RDS, S3, and EKS.
AI Experience: Practical, hands-on experience with modern AI frameworks and techniques, including LLMs or Generative AI applications.
Familiarity with workflow orchestration tools (e.g., Airflow) and ML platforms (e.g., MLflow) is preferred.
Knowledge of classification models, regression models, anomaly detection, boosted models, deep learning, and simulation problems, particularly with large datasets.
Strong problem-solving skills and the ability to work independently on projects.