Machine Learning Engineer

Roper Technologies is seeking a Machine Learning Engineer to help design, build, and deploy advanced AI systems across their portfolio of market-leading software businesses. The role focuses on developing scalable machine learning products and services, with responsibilities that include leading architectural initiatives and mentoring engineers.

Responsibilities:

• Design, build, and deploy machine learning models and AI systems in production environments
• Develop components such as:
• Model inference services
• Data and feature pipelines
• Complex recommendation and matching services
• Vision based analysis systems
• Evaluation and monitoring pipelines
• Optimize models for performance, reliability, and cost efficiency
• Contribute to the development of AI agents and multi-step workflow automation systems
• Build systems that integrate with enterprise tools and APIs
• Implement tool-use frameworks, memory mechanisms, and evaluation loops
• Experiment with LLMs, foundation models, and fine-tuning approaches
• Help translate AI research advances into practical, scalable solutions
• Write high-quality, maintainable, and well-tested code
• Participate in architecture design and technical reviews
• Contribute to CI/CD pipelines and MLOps workflows
• Implement observability and monitoring for AI systems in production
• Follow security, compliance, and responsible AI best practices
• Partner with product, data engineering, and infrastructure teams
• Help identify high-impact AI use cases within portfolio companies
• Support integration of shared AI components into business applications
• Communicate technical tradeoffs clearly to both technical and non-technical stakeholders

Requirements:

• 3+ years of experience in software engineering, data science, or machine learning (more for senior roles)
• Experience building and deploying production software systems
• Strong programming skills in Python (experience in additional languages is a plus)
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
• Understanding of modern AI architectures, including LLM-based systems
• Experience working with cloud environments (AWS, Azure, or GCP)
• Strong problem-solving skills and attention to detail
• Experience with fine tuning, experimentation, etc
• Rapid development using AI tools
• Agent frameworks and orchestration tools
• Distributed systems or microservices architecture
• Model monitoring and evaluation frameworks
• Experience building reusable libraries or shared infrastructure
• Exposure to SaaS products or enterprise software environments
• Background in optimizing models for performance and cost