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Freelance Google Machine Learning Engineer Jobs in Arkansas

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

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Freelance Google Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as a freelance Google Machine Learning Engineer?

To thrive as a Freelance Google Machine Learning Engineer, you need a solid background in computer science, statistics, and machine learning, typically supported by a relevant degree and experience with real-world data projects. Familiarity with Google Cloud Platform (GCP), TensorFlow, and certifications like Google Professional Machine Learning Engineer are commonly required. Strong problem-solving abilities, self-motivation, and effective client communication distinguish top freelancers in this field. These skills and qualifications are crucial for delivering robust machine learning solutions tailored to client needs and efficiently navigating remote, project-based work.

What does a freelance Google Machine Learning Engineer do?

A Freelance Google Machine Learning Engineer is a technical specialist who designs, develops, and deploys machine learning models using Google’s tools and platforms, such as TensorFlow and Google Cloud AI services. They work independently or with clients to solve data-driven problems, build predictive models, and automate processes using machine learning techniques. Their responsibilities may include data preprocessing, feature engineering, model training and evaluation, and integrating models into production systems. Freelancers often manage multiple projects and must stay updated on the latest ML advancements and Google technologies.

What are some common challenges freelance Google Machine Learning Engineers face when working with clients remotely?

Freelance Google Machine Learning Engineers often encounter challenges such as clearly defining project scopes, aligning on deliverables, and managing expectations, especially when working remotely. Communication can be more complex due to time zone differences and varying levels of technical understanding among clients. Staying updated with Google’s latest ML tools and ensuring secure, efficient data sharing are also important. Building strong documentation and regular progress updates can help foster trust and smooth collaboration.

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

AspectFreelance Google Machine Learning EngineerFreelance Data Scientist
CredentialsKnowledge of Google Cloud ML tools, programming skills in Python, TensorFlowStatistical expertise, programming in Python/R, data analysis skills
Work EnvironmentCloud platforms, AI/ML projects, collaboration with developersData analysis, reporting, model development, client communication
Industry UsageTech companies, AI startups, cloud service providersFinance, healthcare, marketing, research organizations

While both roles involve working with data and models, a Freelance Google Machine Learning Engineer specializes in deploying ML solutions on Google Cloud, focusing on AI/ML engineering tasks. A Freelance Data Scientist primarily analyzes data, builds statistical models, and provides insights. The roles overlap in skills but differ in focus and tools used.

What are the most commonly searched types of Google Machine Learning Engineer jobs in Arkansas? The most popular types of Google Machine Learning Engineer jobs in Arkansas are:
What are popular job titles related to Freelance Google Machine Learning Engineer jobs in Arkansas? For Freelance Google Machine Learning Engineer jobs in Arkansas, the most frequently searched job titles are:
What job categories do people searching Freelance Google Machine Learning Engineer jobs in Arkansas look for? The top searched job categories for Freelance Google Machine Learning Engineer jobs in Arkansas are:
What cities in Arkansas are hiring for Freelance Google Machine Learning Engineer jobs? Cities in Arkansas with the most Freelance Google Machine Learning Engineer job openings:

Machine Learning Engineer, Specialist

慨正橡扯

Malvern, AR • On-site

$90 - $120/hr

Other

Posted 3 days ago

New


Job description

We are seeking an experienced Machine Learning Engineer to join our AI/ML Engineering team. You will be responsible for developing and optimizing complex data pipelines, integrating model pipelines, and building scalable AI/ML solutions, including large language models (LLMs). The ideal candidate will possess a robust background in traditional machine learning, applied GenAI, and significant experience with large datasets and AWS cloud-based AI/ML services.

Supports and performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment.

Core Responsibilities
  • Leverages data pipeline designs and supports the development of data pipelines to support model development. Proficient with software tools that develop data pipelines in a distributed computing environment (PySpark, GlueETL).
  • Supports integration of model pipelines in a production environment. Develops understanding of SDLC for model production.
  • Reviews pipeline designs, makes data model design changes as needed. Documents and reviews design changes with data science teams.
  • Supports data discovery & automated ingestion for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs.
  • Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities.
  • Runs model monitoring scripts, follows process for alerts to management as needed. Addresses issues found in data pipelines from model monitoring alerts.
  • Participates in special projects and performs other duties as assigned.
Qualifications
  • Undergraduate degree or equivalent experience; a graduate degree is preferred.
  • Minimum of 5 years of relevant work experience.
  • At least 3 years of hands‑on experience designing ETL pipelines using AWS services (e.g., Glue, SageMaker).
  • Proficiency in programming languages, particularly Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks.
  • Strong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases.
  • Familiarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering, and Model Evaluation.
  • Experience with API design and development is a plus.
  • Solid understanding of software engineering principles, including design patterns, testing, security, and version control.
  • Knowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.
  • Understanding of solution architecture for building end-to-end machine learning data pipelines.
Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

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