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Python Ml Developer Jobs in Florida (NOW HIRING)

Rempli

AI / ML Engineer

Miami, FL · On-site

$95K - $150K/yr

AI / ML Engineer We partner withcompanies pushing the boundaries of technology - and we'relooking ... Strongprogramming skills in Python, R, or similar languages * Experiencewith ML frameworks such as ...

Pyramid Consulting, Inc.

AI/ML Engineer

Sunrise, FL · On-site

$50 - $54/hr

... developer with light AI exposure. * Strong Java Full Stack background is important, but Python is where a lot of the AI/ML work will happen. * Need someone who has integrated AI tools and LLM APIs ...

Dexian

Python Developer with AI

Tampa, FL · On-site

$45.75 - $63/hr

Python Developer with AI/ML Location: Tampa ,FL (Hybrid-2 Days Remote,3 Days Onsite) Contract Job Summary: . Lead hands-on implementation and architecture of Python frameworks for scalable, resilient ...

ReturnPro

AI/ML Engineer

Aventura, FL · On-site

Required: • 5+ years of experience in AI/ML engineering with a strong focus on generative AI, RAG applications, and predictive modeling. • Proficiency in Python and AI/ML libraries like ...

ReturnPro

AI/ML Engineer

Aventura, FL

Required: · 5+ years of experience in AI/ML engineering with a strong focus on generative AI, RAG applications, and predictive modeling. · Proficiency in Python and AI/ML libraries like TensorFlow ...

Arkhya Tech

Python Full Stack Developer

Miami, FL · On-site

Full Stack Developer (Python + React + Azure) Location: Miami, FL (Onsite) Experience 10+ Years ... Handling schema drift & concept drift in ML systems. * Hands-on experience with: * RAG pipelines

Raft

ML Ops Engineer

Tampa, FL · On-site

They are seeking an ML Ops Engineer to design, build, and maintain the infrastructure and pipelines ... Python skills for defining and maintaining ML pipelines • Practical experience with PyTorch ...

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Python Ml Developer information

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI and machine learning systems. While AI automation tools can handle certain tasks, MLEs are essential for creating, optimizing, and interpreting complex models, making complete replacement unlikely in the near term. MLEs need skills in programming, data analysis, and model deployment to adapt to evolving AI technologies.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in deep learning, data science, and programming with tools like Python and TensorFlow. Such roles usually involve leadership, strategic planning, and extensive experience in the field.

Which 3 jobs will survive AI?

For a Python ML Developer, roles that require complex problem-solving, creativity, and human judgment are likely to persist, such as AI research scientist, data scientist, and software engineer. These jobs involve designing, interpreting, and improving AI models, which currently require advanced expertise, critical thinking, and domain knowledge that AI cannot fully replicate. Continuous learning and staying updated with new tools and techniques are essential for long-term career resilience.

What are the key skills and qualifications needed to thrive as a Python ML Developer, and why are they important?

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

Can you do ML in Python?

Yes, Python is widely used for machine learning (ML) development due to its extensive libraries such as TensorFlow, scikit-learn, and PyTorch. Python skills are essential for a Python ML developer to build, train, and deploy ML models efficiently in various environments.
What are popular job titles related to Python Ml Developer jobs in Florida? For Python Ml Developer jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Python Ml Developer jobs in Florida look for? The top searched job categories for Python Ml Developer jobs in Florida are:
What cities in Florida are hiring for Python Ml Developer jobs? Cities in Florida with the most Python Ml Developer job openings:
Python Ml Developer jobs near you
Backend Python Developer - AI/ML

Backend Python Developer - AI/ML

Snowrelic Inc

Orlando, FL • On-site

Full-time

Re-posted 17 days ago

Job description

Job : Backend Python Developer – AI/ML

Location : Orlando preferable, Las Vegas

Skills : Python backend developer, FastAPI, RESTful APIs, Azure, LLM APIs, AI agent frameworks, LangChain, AutoGen, Semantic Kernel, cloud services, vector databases, RAG workflows

job Description : 

Role: Backend Python Developer – AI/ML

Orlando preferable, Las Vegas possible (Onsite)

Required Skills & Experience:

  • 5+ years of experience as a Python backend developer
  • Solid hands-on experience with FastAPI, Flask, or Django REST frameworks
  • Strong knowledge of RESTful APIs, microservices, and asynchronous programming
  • Hands n Experience with Azure 
  • Experience integrating LLM APIs (OpenAI, HuggingFace Inference API, etc.) into real-world applications
  • Experience with AI agent frameworks such as LangChain, AutoGen, or Semantic Kernel
  • Proven ability to deploy containerized apps using Docker, including LLM inference services and agent-based tools
  • Familiarity with vector databases and RAG workflows
  • Exposure to cloud services (AWS, GCP, Azure) and CI/CD pipelines