Machine Leaning Engineering (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.

Responsibilities : ● Independently build, train, and deploy machine learning models for complex projects. ● 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.

Basic Qualifications: ● Bachelor's or Master's 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). ● Data Analytics Experience: Strong background in data analytics, including statistical analysis, data visualization, and reporting to derive business insights. ● 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.