ML Engineer II

Summary

Press Ganey is an experience measurement, data analytics, and insights provider that uses technology, data, and expertise to help organizations improve human experiences. The ML Engineer II will expand and scale machine learning and artificial intelligence capabilities by building production AI agents, integrating AI features into enterprise applications, and supporting reliable AI systems across business units.

Responsibilities

• Build, test, and deploy production-ready AI agents and agentic workflows that integrate seamlessly with enterprise applications and business processes
• Implement robust evaluation, monitoring, guardrail, and safety frameworks defined by senior team members to de-risk LLM-powered applications
• Develop and maintain AI-enabled features within our existing Java/Spring platform and distributed microservices ecosystem
• Collaborate with AI Scientists and ML Engineers to productionize models and translate experimentation into reliable software
• Staying up to date with emerging AI technologies and recommending pragmatic adoption strategies that improve product capabilities and developer productivity
• Work closely with, and incorporate feedback from other specialists, tech-ops, and product managers
• Participate in design reviews, technical discussions, and requirement planning to continuously grow technical skills and domain knowledge
• Attend daily stand-up meetings, collaborate with your peers, prioritize features, and work with a sense of urgency to deliver value to your customers
• Finding quick ways to prototype and test possible solutions to large problems

Skills

• Bachelor's degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience
• 2–5 years of hands-on experience as a Machine Learning Engineer, with a proven track record of designing, building, and optimizing ML models using frameworks like PyTorch or TensorFlow for production environments
• Solid Computer Science fundamentals in data structures, algorithm design, complexity analysis, and performance optimization
• Strong proficiency in object-oriented design and development using modern programming languages such as Python, Java, or C#
• Hands-on experience developing, testing, and supporting applied machine learning services within enterprise software ecosystems
• Practical experience building and maintaining software components for AI-enabled applications or distributed systems
• Hands-on experience integrating AI capabilities into existing enterprise applications rather than building standalone AI prototypes
• Experience implementing and deploying production-grade LLM applications using frameworks such as LangChain, LangGraph, Spring AI, Model Context Protocol (MCP), or similar technologies
• Solid working knowledge of Retrieval-Augmented Generation (RAG), tool calling, prompt engineering, and core AI agent principles
• Experience in observability, CI/CD, automated testing, and production operations for AI applications
• Excellent communication and collaboration skills
• Experience with Databricks, vector search technologies, embedding models, conversational AI, and evaluation strategies for AI agents
• Experience working with AI developer tooling and platform capabilities that accelerate feature delivery
• Experience building large-scale Java microservices using Spring Boot
• Experience with cloud-native platforms (AWS, Azure, or GCP), Kubernetes, and containerized deployments
• Familiarity with model serving, inference optimization, caching strategies, and cost optimization for enterprise AI systems

Qualifications

Must Haves

• Bachelor's degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience
• 2–5 years of hands-on experience as a Machine Learning Engineer, with a proven track record of designing, building, and optimizing ML models using frameworks like PyTorch or TensorFlow for production environments
• Solid Computer Science fundamentals in data structures, algorithm design, complexity analysis, and performance optimization
• Strong proficiency in object-oriented design and development using modern programming languages such as Python, Java, or C#
• Hands-on experience developing, testing, and supporting applied machine learning services within enterprise software ecosystems
• Practical experience building and maintaining software components for AI-enabled applications or distributed systems
• Hands-on experience integrating AI capabilities into existing enterprise applications rather than building standalone AI prototypes
• Experience implementing and deploying production-grade LLM applications using frameworks such as LangChain, LangGraph, Spring AI, Model Context Protocol (MCP), or similar technologies
• Solid working knowledge of Retrieval-Augmented Generation (RAG), tool calling, prompt engineering, and core AI agent principles
• Experience in observability, CI/CD, automated testing, and production operations for AI applications
• Excellent communication and collaboration skills

Nice to Haves

• Experience with Databricks, vector search technologies, embedding models, conversational AI, and evaluation strategies for AI agents
• Experience working with AI developer tooling and platform capabilities that accelerate feature delivery
• Experience building large-scale Java microservices using Spring Boot
• Experience with cloud-native platforms (AWS, Azure, or GCP), Kubernetes, and containerized deployments
• Familiarity with model serving, inference optimization, caching strategies, and cost optimization for enterprise AI systems

Benefits

• Remote work
• Successful candidates are eligible to receive a discretionary bonus or commission tied to achieved results.