> Markdown version of [/jobs/ext/3891624-staff-mlops-engineer-remote-in-ri](https://www.wearedevelopers.com/jobs/ext/3891624-staff-mlops-engineer-remote-in-ri). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Staff MLOps Engineer (Remote in RI)

- **Company:** Motion Recruitment Partners LLC.
- **Location:** Providence, RI, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Batch Processing, Cloud Computing, Cloud Engineering, Continuous Integration, Customer Data Management, Distributed Systems, Python (Programming Language), Machine Learning, Software Product Management, Software Engineering, TypeScript, Management of Software Versions, AI Infrastructure, AWS Cdk, Data Logging, Large Language Models, Model Validation, Generative AI, Backend, Cloudformation, Containerization, AI Platforms, Kubernetes, Real-time Inference, Machine Learning Operations, Api Design, Dynatrace, Automation Anywhere, Docker
- **Published:** October 23, 2026
- **Apply:** https://www.dice.com/job-detail/8122b3d5-532c-49cf-a473-876f0c5b5d47

## About the Role

As a senior technical contributor, you will partner closely with software engineers, data scientists, AI specialists, and cloud teams to develop robust solutions that enable reliable, secure, and efficient machine learning workloads in production. Required Skills & Experience

 * 10+ years of professional experience in software engineering, platform engineering, or cloud infrastructure
 * Strong experience designing and operating distributed systems in production environments
 * Proven track record leading complex technical initiatives and driving engineering best practices
 * Significant experience building and supporting MLOps platforms for production workloads
 * Hands-on experience deploying, monitoring, and maintaining large language model (LLM) applications
 * Deep understanding of machine learning lifecycle management, model deployment, and governance
 * Experience implementing Retrieval-Augmented Generation (RAG) solutions and vector-based retrieval systems
 * Advanced proficiency with Python and TypeScript
 * Strong experience developing APIs and backend services
 * Expertise with AWS services and cloud-native architectures
 * Experience with Docker and containerized application deployment
 * Familiarity with Infrastructure as Code technologies such as AWS CDK or CloudFormation
 * Strong knowledge of observability practices, monitoring solutions, and distributed tracing
 * Experience securing multi-tenant environments and protecting regulated or sensitive data
 * Ability to collaborate across teams and influence technical decisions

Desired Skills & Experience

 * Experience supporting agent-based AI workflows in production
 * Familiarity with modern LLM orchestration frameworks and tooling
 * Knowledge of AI infrastructure optimization, inference performance, and resource utilization
 * Experience designing model evaluation frameworks and testing pipelines
 * Exposure to multimodal AI systems involving image or scientific datasets
 * Understanding of model tuning, distillation, and inference optimization techniques
 * Experience working within regulated industries and compliance-driven environments
 * Background supporting scientific, life sciences, pharmaceutical, or healthcare technology platforms
 * Experience measuring and managing AI platform costs at scale

## Description

What You Will Be Doing Daily Responsibilities

 * Build and enhance the organization's machine learning operations platform
 * Develop scalable infrastructure supporting AI and machine learning applications
 * Manage model deployment workflows, versioning processes, and release strategies
 * Design solutions for both real-time inference and large-scale batch processing
 * Implement secure architectures that support customer data isolation and governance requirements
 * Create monitoring, logging, and alerting capabilities for production AI systems
 * Establish validation and evaluation processes that improve AI reliability and quality
 * Drive platform scalability, availability, performance, and operational efficiency
 * Collaborate with engineering and data teams to support AI product development
 * Contribute to CI/CD automation and infrastructure deployment initiatives
 * Mentor fellow engineers and promote engineering best practices
 * Evaluate emerging technologies and recommend enhancements to the AI platform