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Ml Platform Engineer Jobs in Florida (NOW HIRING)

Deloitte

AI Engineer Consultant

Tampa, FL · Hybrid

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... solutions. This role is hands-on and delivery-oriented: you will ship production pipelines and ...

Deloitte

AI Engineer Consultant

Miami, FL · Hybrid

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... solutions. This role is hands-on and delivery-oriented: you will ship production pipelines and ...

Deloitte

AI Engineer Consultant

Jacksonville, FL · Hybrid

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... solutions. This role is hands-on and delivery-oriented: you will ship production pipelines and ...

Parsons

$98K - $133K/yr

Evaluate emerging cloud-native, automation, AI/ML, and infrastructure technologies for mission ... Experience in platform engineering, systems engineering, cloud engineering, infrastructure ...

Booz Allen Hamilton

DevOps Platform Engineer

Eglin, FL

$62K - $141K/yr

... s Platform Engineer The Opportunity: Are you looking for an opportunity to make a difference? What ... Experience with AI/ML/LLM security such as Hidden Layer * Experience automating, monitoring and ...

Raft

Lead Engineer

Tampa, FL · On-site

$100K - $132K/yr

You will operate at the intersection of distributed systems, AI/ML platform engineering, Kubernetes-native infrastructure, and data-intensive application development, balancing rapid mission delivery ...

Raft Company Website

Lead Engineer

Tampa, FL

$96K - $127K/yr

You will operate at the intersection of distributed systems, AI/ML platform engineering, Kubernetes-native infrastructure, and data-intensive application development, balancing rapid mission delivery ...

Armaninollp

Data & AI Platform Engineer

Boca Raton, FL

$108K - $130K/yr

Minimum 2 years in a data engineering, platform engineering, analytics engineering, or cloud ... Demonstrated experience with AI/ML/GenAI enablement (model lifecycle, AI Search, Azure OpenAI ...

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Ml Platform Engineer information

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$24

$47

$70

How much do ml platform engineer jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for ml platform engineer in Florida is $47.79, according to ZipRecruiter salary data. Most workers in this role earn between $37.74 and $55.14 per hour, depending on experience, location, and employer.

What is an ML Platform Engineer?

ML Platform Engineers are specialized software engineers who design, build, and maintain the infrastructure and tools needed to support the development, deployment, and scaling of machine learning models. They bridge the gap between data science and production engineering by automating model training, monitoring, versioning, and serving. Their work enables data scientists to focus on modeling while ensuring that ML solutions are reliable, reproducible, and scalable in real-world environments.

What skills and qualifications are needed to thrive as an ML Platform Engineer?

To thrive as an ML Platform Engineer, you need a strong background in computer science, software engineering, and machine learning concepts, often supported by a degree in a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), containerization (Docker, Kubernetes), CI/CD pipelines, and knowledge of ML frameworks (TensorFlow, PyTorch) are commonly required. Collaboration, problem-solving, and strong communication skills help you work efficiently with data scientists, engineers, and stakeholders. These skills ensure the development, scalability, and reliability of robust ML infrastructure that empowers teams to deploy and manage models effectively.

How does an ML Platform Engineer typically collaborate with data scientists and software engineers within a company?

ML Platform Engineers work closely with both data scientists and software engineers to streamline the process of developing, deploying, and maintaining machine learning models. They provide the infrastructure and tools necessary for data scientists to build and experiment with models efficiently, while ensuring seamless integration with production systems managed by software engineers. Regular communication, participation in cross-functional meetings, and shared project management tools are common ways teams collaborate. This close collaboration helps to bridge the gap between research and production, ensuring robust, scalable, and reliable ML solutions.

What is the difference between Ml Platform Engineer vs Data Scientist?

AspectML Platform EngineerData Scientist
Required credentialsBachelor's/Master's in CS, Engineering, or related; experience with cloud platformsBachelor's/Master's in Statistics, Math, or CS; strong programming skills
Work environmentBuilds and maintains ML infrastructure, collaborates with engineering teamsAnalyzes data, develops models, and interprets results
Industry usageTech companies, AI startups, enterprises deploying ML systemsResearch institutions, tech firms, data-driven organizations

ML Platform Engineers focus on developing and maintaining the infrastructure that supports machine learning models, while Data Scientists primarily analyze data and build models. Both roles often collaborate but serve different functions within the AI and data ecosystem.

What are popular job titles related to Ml Platform Engineer jobs in Florida?

For Ml Platform Engineer jobs in Florida, the most frequently searched job titles are:

What job categories do people searching Ml Platform Engineer jobs in Florida look for?

The top searched job categories for Ml Platform Engineer jobs in Florida are:

What cities in Florida are hiring for Ml Platform Engineer jobs?

Cities in Florida with the most Ml Platform Engineer job openings:

Ml Platform Engineer jobs near you
Infographic showing various Ml Platform Engineer job openings in Florida as of August 2026, with employment types broken down into 57% Full Time, 40% Part Time, and 3% Contract. Highlights an 77% Physical, 3% Hybrid, and 20% Remote job distribution, with an average salary of $99,409 per year, or $47.8 per hour.

Information Technology_USA - USA_Developer

Real Soft, Inc.

Jacksonville, FL • On-site

$100K - $136K/yr

Contractor

Re-posted 22 days ago

Job description

2 Candidate Submittal Slots, New High Level PolicyBill Rate - MSP Owner: Rob FintonLocation: White Plains, NY or Fort Lauderdale, FL - Position can be Onsite or RemoteDuration: 6 monthsGBaMS ReqID: 10914533Competencies: 10+ years experience requiredDigital : Machine LearningDigital : DevOpsQuick JD:Senior DevOps Engineer with deep expertise in designing, automating, and operating cloud-based AI/ML platforms.The ideal candidate will have hands-on experience building scalable, secure, and production-grade machine learning environments, with a strong focus on AWS SageMaker, MLOps practices, and modern cloud infrastructure.Experience with Generative AI platforms and services, including Amazon Bedrock, Azure OpenAI, vector databases, RAG architectures, and LLM deployment patterns.Familiarity with ML frameworks such as TensorFlow, PyTorch, MLflow, Kubeflow, or similar technologies.Experience supporting GPU-based workloads and optimizing infrastructure for AI model training and inference.This role will be instrumental in establishing and evolving the organization's AI/ML platform capabilities by delivering robust, automated, and secure cloud infrastructure that accelerates innovation while ensuring operational excellence.Role Summary : Senior DevOps Engineer - AI/ML Platform Engineering (AWS/Azure)This role will be instrumental in establishing and evolving the organization's AI/ML platform capabilities by delivering robust, automated, and secure cloud infrastructure that accelerates innovation while ensuring operational excellence.Senior DevOps Engineer with deep expertise in designing, automating, and operating cloud-based AI/ML platforms. The ideal candidate will have hands-on experience building scalable, secure, and production-grade machine learning environments, with a strong focus on AWS SageMaker, MLOps practices, and modern cloud infrastructure.Key Responsibilities & Qualifications• Extensive hands-on experience designing, implementing, and managing AI/ML infrastructure and MLOps platforms in AWS and/or Azure.• Strong expertise with AWS SageMaker, ML lifecycle management, model training and deployment pipelines, feature stores, model monitoring, and platform automation.• Proven experience building and supporting enterprise-scale MLOps ecosystems, including CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation/Bicep), containerization, and cloud-native architectures.• Experience integrating AI/ML platforms with modern data ecosystems, including technologies such as Snowflake, data lakes, streaming services, and analytics platforms.• Deep knowledge of cloud services including AWS ECS, EKS/Kubernetes, networking, security, IAM, observability, and high-availability architectures.• Responsible for enabling secure, scalable, resilient, and production-ready AI/ML platforms that support Data Science, Generative AI, and advanced analytics initiatives.• Serve as a trusted technical advisor to engineering, data science, and platform teams, providing architectural guidance, operational best practices, and real-time troubleshooting support.• Demonstrated ability to rapidly assess platform, infrastructure, and deployment challenges and recommend scalable, cost-effective, and secure solutions.• Strong understanding of DevSecOps principles, cloud governance, compliance requirements, and automation strategies for enterprise AI workloads.Excellent communication and collaboration skills, with the ability to bridge the gap between Data Science, Engineering, Operations, and Cloud Infrastructure teams.Preferred Experience• Experience with Generative AI platforms and services, including Amazon Bedrock, Azure OpenAI, vector databases, RAG architectures, and LLM deployment patterns.• Familiarity with ML frameworks such as TensorFlow, PyTorch, MLflow, Kubeflow, or similar technologies.• Experience supporting GPU-based workloads and optimizing infrastructure for AI model training and inference.

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