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Mlops Machine Learning Engineer Jobs in Florida (NOW HIRING)

Intraedge

Machine Learning Engineer

Sunrise, FL · Hybrid

Machine Learning Engineer - Generative Al Long term contract Sunrise, FL (Hybrid-3 days onsite) Direct client- Immediate client interview We are seeking a Machine Learning Engineer to design, build ...

OptiTrack

Machine Learning & Operations Engineer

Miami, FL · Remote

$66K - $89K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning.

OptiTrack

Machine Learning & Operations Engineer

Miami, FL · Remote

$66K - $89K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning.

Citadel

Machine Learning Engineer

Miami, FL

Skills and Preferred Qualifications * 2+ years of experience in machine learning and software development. * Strong engineering skills, including Python, CUDA, C++. * Experience building distributed ...

Indeed

Machine Learning Engineer

Miami, FL

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source ...

ClifyX

Sr. Machine Learning Engineer

Bradenton, FL · Remote

$111K - $146K/yr

Sr. Machine Learning Engineer The Sr. Machine Learning Engineer collaborates with the team of Data Scientists and Data Analysts in creating scalable, data-driven, customer-centric solutions, capable ...

SynergisticIT

Machine Learning Engineer

Orlando, FL

Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers for full time positions ...

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Mlops Machine Learning Engineer information

What does an MLOps Machine Learning Engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps Machine Learning Engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

What are the key skills and qualifications needed to thrive as an MLOps Machine Learning Engineer, and why are they important?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
What are popular job titles related to Mlops Machine Learning Engineer jobs in Florida? For Mlops Machine Learning Engineer jobs in Florida, the most frequently searched job titles are:
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Mlops Machine Learning Engineer jobs near you
Machine Learning Engineer

Machine Learning Engineer

Roper Technologies

Sarasota, FL

Full-time

Posted yesterday

Roper Technologies rating

8.2

Company rating: 8.2 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

75th of 186 rated software companies

Job description

Roper Technologies is seeking a Machine Learning Engineer to help design, build, and deploy advanced AI systems across our portfolio of market-leading software businesses. 
This role will focus on developing scalable machine learning products and services, shared AI components, and intelligent agents that drive meaningful business impact. Depending on experience level, the role may involve leading architectural initiatives, mentoring engineers, and shaping technical strategy. 
 
We are looking for hands-on engineers who are excited about building production-grade AI systems—not just prototypes—and who thrive in a high-impact, applied environment. Candidates who have demonstrated ability to think through product as well as engineering are highly desired.
 
What You’ll Do 
 
AI & ML System Development 
  • 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      
Intelligent Agents & Applied AI 
  • 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 
Engineering Excellence 
  • 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 
Cross-Functional Collaboration 
  • 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 
Qualifications 
 
We welcome candidates across a range of experience levels. The scope and seniority of responsibilities will scale accordingly.  
Required 
  • 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 
 
Preferred  
  • 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
Leveling & Growth 
 
We are hiring across multiple experience levels: 
  • Intermediate ML Engineer – Contributes independently to projects, builds production features, collaborates cross-functionally. 
  • Senior ML Engineer – Owns complex systems end-to-end, drives architectural decisions, mentors others. 
  • Principal / Staff ML Engineer – Defines technical direction, leads cross-portfolio initiatives, designs shared frameworks and scalable AI infrastructure. 
Level and compensation will be determined based on experience and demonstrated expertise. 
 
What We Value 
  • Strong engineering fundamentals 
  • Practical, impact-driven AI development 
  • Curiosity and willingness to experiment responsibly 
  • Ownership mindset and bias toward execution 
  • Ability to balance innovation with reliability 
Why Join Roper 
  • Work on high-impact AI systems across a diverse portfolio of leading software businesses 
  • Build reusable infrastructure that scales across industries 
  • Collaborate with experienced engineering and executive leadership 
  • Shape the next generation of intelligent enterprise software 

Apply