Staff ML Engineer

Press Ganey

. Design, build, and deploy production-ready AI agents and agentic workflows integrated with enterprise applications and business processes .

Posted 9/23/2026full-timeRemote • United StatesLead💰 $130,000 - $190,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying scalable AI-enabled applications and distributed systems, with strong proficiency in Python, Java, and the Spring ecosystem. Proven ability to lead architectural decisions and mentor teams while integrating AI capabilities into enterprise applications.

Highest-signal resume keywords

Python ProficiencyJava ExpertiseSpring Boot DevelopmentAI Agent ArchitectureObservability and CI/CD

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Application DevelopmentDistributed Systems EngineeringProduction-Grade LLM ApplicationsLangChainLangGraphModel Context Protocol (MCP)Retrieval-Augmented Generation (RAG)Automated TestingMicroservices ArchitectureEnterprise Application Integration

Soft Skills

Excellent CommunicationCollaborationMentoring

Tools & Technologies

AWSAzureGCPKubernetesDatabricksContainerized Deployments

Industry Keywords

AI AgentsAgentic WorkflowsEnterprise-Scale ApplicationsTechnical StrategyEvaluation Strategies

Tech Stack

Tools & technologies

AWSAzureDistributed SystemsGoogle Cloud PlatformJavaKubernetesMicroservicesPythonSpringSpring BootSpringBoot

About the role

Key responsibilities & impact

Design, build, and deploy production-ready AI agents and agentic workflows integrated with enterprise applications and business processes
Establish evaluation, monitoring, guardrail, and safety frameworks for LLM-powered applications
Architect and implement scalable AI-enabled capabilities within the existing Java/Spring platform and distributed microservices ecosystem
Establish engineering best practices for AI Agent systems, including observability, testing, deployment, versioning, and operational excellence
Drive architectural decisions across AI services, microservices, APIs, and enterprise application integrations
Collaborate with AI Scientists and ML Engineers to productionize models and translate experimentation into reliable software
Mentor engineers, lead architectural reviews, and influence technical strategy across teams
Evaluate emerging AI technologies and recommend pragmatic adoption strategies to improve product capabilities and developer productivity

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
8+ years of professional software engineering experience, including leading complex technical initiatives in distributed systems
Strong proficiency in Python, Java, and the Spring ecosystem, including Spring Boot and Spring Framework
Experience building and evolving enterprise-scale applications
Deep expertise building scalable, production-grade AI-enabled applications and distributed systems
Experience architecting and deploying AI agents, agentic workflows, and LLM-powered applications at enterprise scale
Hands-on experience integrating AI capabilities into existing enterprise applications
Extensive experience designing and deploying production-grade LLM applications using LangChain, LangGraph, Spring AI, Model Context Protocol (MCP), or similar technologies
Strong understanding of Retrieval-Augmented Generation (RAG), tool calling, MCP, prompt engineering, and agent orchestration
Expertise in observability, CI/CD, automated testing, and production operations for AI applications
Proven ability to navigate ambiguity, reduce technical risk, and influence architectural direction across cross-functional teams
Excellent communication, collaboration, and mentoring skills
Experience with Databricks, vector search technologies, embedding models, conversational AI, and evaluation strategies for AI agents preferred
Experience building AI infrastructure, developer tooling, and platform capabilities preferred
Experience architecting and maintaining large-scale Java microservices using Spring Boot preferred
Experience with AWS, Azure, or GCP, Kubernetes, and containerized deployments preferred
Familiarity with model serving, inference optimization, caching strategies, and cost optimization for enterprise AI systems preferred

Benefits

Comp & perks

Competitive benefits package
Discretionary bonus or commission tied to achieved results
Reasonable accommodations for qualified individuals with disabilities or disabled veterans in the hiring process