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Remote Aws Machine Learning Jobs in Texas (NOW HIRING)

Photon

AWS Gen AI / ML Engineer - Plano, TX

Plano, TX · On-site +1

AWS Gen AI / ML Engineer We are seeking an AWS Gen AI / ML Engineer to design, deploy, and optimize cloud-native machine-learning systems that power our next-generation predictive-automation platform.

Neos Consulting

Data Architect

Austin, TX · On-site

$63.25 - $81.25/hr

REMOTE - ONLY CANDIDATES CURRENTLY RESIDING IN THE AUSTIN, TEXAS WILL BE CONSIDERED*** No calls, no ... Develop and operationalize scalable architectures for collaborative notebooks, machine learning ...

Deckers

United States (Remote) Interested applicants must reside in one of the following approved states ... AWS certifications (Data, Machine Learning, or Solution Architecture) are a plus * 8 to 12 years of ...

Tiger Analytics Inc.

Lead Data Engineer - AWS

Dallas, TX · On-site +1

$101K - $133K/yr

Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted ... LLM Ops: Integrate AWS Step Functions and SageMaker Pipelines to automate the fine-tuning and ...

Sparks Group

Data Engineer

Dallas, TX · On-site +1

$113K - $136K/yr

Exposure to machine learning pipelines, MLOps concepts, or AI-driven data workflows preferred ... is Hybrid Remote. We offer several comprehensive benefits package including health and life ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting, Random Forest * Distributed computing tools and cloud technology (AWS) QUALIFICATIONS * Degree in Data Science ...

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Remote Aws Machine Learning information

What are the key skills and qualifications needed to thrive as a Remote AWS Machine Learning Engineer, and why are they important?

To thrive as a Remote AWS Machine Learning Engineer, you need a strong background in machine learning algorithms, statistical analysis, and proficiency in programming languages such as Python, often supported by a relevant degree or certification. Familiarity with AWS services like SageMaker, Lambda, and EC2, as well as experience using cloud-based ML tools and AWS Certified Machine Learning credentials, is typically required. Excellent problem-solving skills, self-motivation, and clear written communication are valuable soft skills for remote collaboration and project management. These skills ensure effective model development, seamless deployment on cloud infrastructure, and successful remote teamwork in delivering scalable ML solutions.

What are remote AWS Machine Learning jobs?

Remote AWS Machine Learning jobs involve working with Amazon Web Services' suite of machine learning tools and services, such as SageMaker, to build, train, and deploy machine learning models. These positions allow professionals to work from anywhere, collaborating with teams virtually while leveraging AWS infrastructure to solve data-driven problems. Responsibilities often include data preprocessing, model development, and deploying scalable solutions in the cloud. Typical job titles may include Machine Learning Engineer, Data Scientist, or AI Developer, all with a focus on AWS technologies. These roles require strong programming skills, experience with cloud computing, and a background in machine learning or data science.

What is the difference between Remote Aws Machine Learning vs Remote Data Scientist?

AspectRemote Aws Machine LearningRemote Data Scientist
Required CredentialsAWS certifications, machine learning coursesStatistics, data analysis, programming skills
Work EnvironmentCloud platforms, AWS services, remote teamsData analysis, modeling, research in remote settings
Industry UsageTech, finance, healthcare using AWS ML toolsResearch, consulting, analytics across industries

Remote AWS Machine Learning specialists focus on deploying machine learning models using AWS cloud services, requiring AWS certifications and cloud expertise. Remote Data Scientists analyze data, build models, and interpret results, often with a stronger emphasis on statistics and programming. While both roles work remotely and involve data, AWS Machine Learning roles are more cloud and deployment-oriented, whereas Data Scientists focus on data analysis and research.

What are some common challenges faced by remote AWS Machine Learning engineers, and how can they be addressed?

Remote AWS Machine Learning engineers often face challenges related to communication and collaboration, especially when working across different time zones and with cross-functional teams. Ensuring secure access to data and cloud resources is another key concern, given the sensitive nature of many machine learning projects. To overcome these challenges, engineers should leverage AWS collaboration tools, maintain clear documentation, and participate in regular virtual meetings. Additionally, setting up robust security protocols and using AWS Identity and Access Management (IAM) helps safeguard project assets while enabling effective teamwork.
What are the most commonly searched types of Aws Machine Learning jobs in Texas? The most popular types of Aws Machine Learning jobs in Texas are:
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Remote Aws Machine Learning jobs near you
Software Engineer - AI Specialist | Remote

Software Engineer - AI Specialist | Remote

GigWorld Talent Solutions

Dallas, TX • On-site, Remote

Other

Posted 10 days ago

Job description

Job Description Software Engineer - AI Specialist | Remote Permanent, Full-Time We are supporting an innovative technology firm dedicated to building AI-driven solutions that drive efficiency and transformation across industries. We are seeking a highly skilled Software Engineer specializing in Artificial Intelligence to develop, optimize, and deploy AI-powered applications. Responsibilities: Design, develop, and implement AI models and machine learning algorithms.

Collaborate with cross-functional teams to integrate AI solutions into existing platforms. Optimize AI models for performance, scalability, and efficiency. Research and apply the latest advancements in AI and deep learning.

Develop and maintain data pipelines and AI-driven analytics systems. Ensure AI models are robust, ethical, and aligned with best practices. Troubleshoot and improve AI-based applications as needed.

Stay updated on emerging AI trends and technologies. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience in AI and machine learning development.

Proficiency in programming languages such as Python, Java, or C++. Strong understanding of AI frameworks and libraries (TensorFlow, PyTorch, Scikit-learn, etc.). Experience with natural language processing (NLP), computer vision, or predictive analytics

Knowledge of data science methodologies and model evaluation techniques. Experience with cloud platforms and AI services (AWS, Azure, GCP). Ability to work independently and collaboratively in a fast-paced environment.

Preferred Qualifications: Experience with reinforcement learning and generative AI models. Familiarity with AI ethics, bias mitigation, and explainability techniques. Contribution to open-source AI projects.

Understanding of big data technologies and distributed computing. Benefits: Competitive salary and performance-based incentives. Flexible work schedule and remote work opportunities.

Professional development and continuous learning resources. Opportunity to work with a passionate and innovative team in a fast-growing industry.

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