Production ML Engineer – Generative AI

H&R Block; Missouri; On-site; USD 91,000 - 146,000; Full time

Job summary

H&R Block is seeking a Machine Learning Engineer to design, develop, and deploy ML models, including generative AI models, for real-world business challenges. You will work with ML and MLOps engineers across cross-functional teams to deliver scalable AI solutions and seamless production integration.

The ideal candidate has hands-on experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, spaCy) and strong Python skills, with a collaborative mindset and a focus on continuous learning.

Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, or related field.
• 3 years total experience.
• Hands-on experience with ML frameworks such as scikit-learn, TensorFlow, PyTorch, and spaCy.
• Proficiency in Python and familiarity with ML development workflows.
• Experience with generative AI is a plus.

Responsibilities

• Design, build, and deploy machine learning models—including generative AI models—to address complex business problems.
• Preprocess, clean, and analyze structured and unstructured data for ML pipelines.
• Tune and optimize models for accuracy, efficiency, and scalability in production environments.
• Collaborate with MLOps engineers to ensure smooth deployment, monitoring, and maintenance of ML models.
• Stay informed on the latest advancements in ML, DL, and generative AI technologies and apply them where appropriate.
• Contribute to a collaborative engineering culture by sharing knowledge and participating in code reviews.

Skills

Python

Generative AI

Education

Bachelor's degree in CS/Stats/Math

Master's or PhD in CS/Stats/Math

Tools

scikit-learn

TensorFlow

PyTorch

spaCy

Job description

H&R Block is seeking a Machine Learning Engineer to design, develop, and deploy ML models, including generative AI models, for real-world business challenges. You will work with ML and MLOps engineers across cross-functional teams to deliver scalable AI solutions and seamless production integration.

The ideal candidate has hands-on experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, spaCy) and strong Python skills, with a collaborative mindset and a focus on continuous learning.