Senior Machine Learning Engineer 

Role Overview 

We are seeking a Senior Machine Learning Engineer to design, build, and deploy scalable machine learning solutions that drive business impact across a variety of use cases. 

You will work closely with data scientists, data engineers, and business stakeholders to translate complex problems into robust machine learning systems deployed in production. The ideal candidate combines strong machine learning expertise, software engineering best practices, and experience building scalable cloud-based ML systems. 

What You Will Do 

• Design, develop, and deploy machine learning models and solutions to solve complex business problems. 

• Build and maintain production-grade ML pipelines for model training, evaluation, inference, and monitoring. 

• Perform data transformation and feature engineering to create reliable and scalable input features for machine learning models. 

• Implement model monitoring and drift detection frameworks to track data drift, feature drift, and model performance degradation in production. 

• Develop scalable APIs and ML services using frameworks such as FastAPI to integrate ML models into business applications. 

• Apply strong software engineering principles, including object-oriented programming, modular design, and code maintainability. 

• Implement unit and integration tests using frameworks such as pytest to ensure reliability and maintainability of ML systems. 

• Deploy and manage ML solutions in cloud environments, preferably Microsoft Azure. 

• Work with large-scale enterprise data platforms such as Snowflake and collaborate with data engineering teams to build reliable data pipelines. 

• Optimize model training and performance using distributed computing frameworks such as Ray and Dask. 

• Use Optuna or similar tools for hyperparameter tuning and model optimization. 

• Explore and implement Large Language Model (LLM) based solutions to address business problems such as knowledge retrieval, decision support, and workflow automation. 

• Participate in code reviews, system design discussions, and continuous improvement of engineering standards. 

• Collaborate closely with cross-functional teams including business, analytics, data engineering, and technology teams to deliver high-impact solutions. 

Qualifications 

• Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative field. 

• 4+ years of experience building and deploying machine learning models and systems in production environments. 

• Strong proficiency in Python for machine learning and software development. 

• Strong understanding of object-oriented programming (OOP) and software design principles. 

• Experience building APIs using frameworks such as FastAPI or similar Python web frameworks. 

• Experience implementing unit testing using frameworks such as pytest. 

• Strong understanding of data transformation, feature engineering, and feature pipeline development. 

• Experience implementing model monitoring, drift detection, and model performance tracking in production environments. 

• Experience working with cloud platforms, preferably Microsoft Azure. 

• Experience working with data lake or modern data platforms such as Snowflake. 

• Strong experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow. 

• Experience working with large-scale datasets and building scalable ML pipelines. 

• Familiarity with Large Language Models (LLMs) and their application to solve business problems. 

Preferred Qualifications 

• Experience with distributed computing frameworks such as Ray or Dask. 

• Experience with Optuna or similar hyperparameter optimization tools. 

• Experience with containerization technologies such as Docker. 

• Experience working on end-to-end ML systems and production deployments across different domains. 

Soft Skills 

• Strong communication and presentation skills, with the ability to explain complex technical concepts to non-technical stakeholders. 

• Ability to collaborate effectively across cross-functional teams including business, analytics, and engineering. 

• Strong problem-solving mindset and ownership of end-to-end solutions. 

• Ability to manage multiple priorities in a fast-paced, collaborative environment. 

• Demonstrated ability to work with stakeholders to translate business problems into analytical solutions. 

Kraft Heinz is an Equal Opportunity Employer – Underrepresented Ethnic Minority Groups/Women/Veterans/Individuals with Disabilities/Sexual Orientation/Gender Identity and other protected classes.