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Junior Aws Machine Learning Jobs in Arizona (NOW HIRING)

Technical Skills: · Python -- strong proficiency · Machine Learning and Deep Learning · ... AWS / Azure / Google Cloud Platform · Familiarity with PyTorch or TensorFlow · Understanding of ...

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

Provide technical leadership to junior scientists, guiding the transition of R&D concepts into ... in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant ...

Senior Machine Learning Scientist

Scottsdale, AZ · On-site

$92K - $125K/yr

Provide technical leadership to junior scientists, guiding the transition of R&D concepts into ... in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant ...

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Junior Aws Machine Learning information

What is a junior AWS machine learning engineer?

Junior AWS Machine Learning engineers are entry-level professionals who work with Amazon Web Services (AWS) to develop, deploy, and maintain machine learning models. They assist in data preparation, model training, and integration of AI solutions using AWS tools such as SageMaker, Lambda, and S3. These engineers often collaborate with data scientists and software teams to implement predictive analytics and automation solutions on the AWS cloud platform. Their role typically involves learning best practices for cloud security, data handling, and scalable machine learning deployment.

What are the key skills and qualifications needed to thrive as a junior AWS machine learning engineer?

To thrive as a Junior AWS Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of machine learning concepts, and a relevant degree in computer science or a related field. Familiarity with AWS services like SageMaker, Lambda, and S3, as well as certifications such as AWS Certified Machine Learning – Specialty, are highly valuable. Strong problem-solving skills, attention to detail, and the ability to communicate technical ideas clearly help you stand out in this role. These skills and qualities are crucial for efficiently developing, deploying, and maintaining machine learning solutions on AWS in collaborative, fast-paced environments.

What are some common challenges faced by junior AWS machine learning engineers when deploying models to production environments?

Junior AWS Machine Learning Engineers often encounter challenges such as managing the scalability of their models, ensuring data security and compliance in the cloud, and integrating machine learning pipelines with existing AWS services. Since production environments require high reliability, newcomers may also need to learn how to monitor model performance and troubleshoot issues using AWS tools like SageMaker and CloudWatch. Collaborating closely with data engineers and DevOps teams is essential to streamline deployment and maintain model accuracy over time.

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

AspectJunior Aws Machine LearningData Scientist
Required CredentialsBasic AWS certifications, entry-level ML knowledgeAdvanced degrees, certifications like AWS, data analysis skills
Work EnvironmentCloud platforms, machine learning projects, collaborative teamsData analysis, modeling, research, cross-functional teams
Employer & Industry UsageTech companies, startups, cloud service providersFinance, healthcare, tech, research institutions

Junior AWS Machine Learning roles focus on implementing ML models using AWS tools with foundational knowledge, while Data Scientists typically handle broader data analysis, modeling, and research tasks. The roles overlap in cloud-based ML work but differ in scope and experience level.

What are the most commonly searched types of Aws Machine Learning jobs in Arizona?

The most popular types of Aws Machine Learning jobs in Arizona are:

What cities in Arizona are hiring for Junior Aws Machine Learning jobs?

Cities in Arizona with the most Junior Aws Machine Learning job openings:

Junior AI Engineer

Synergie Systems

Phoenix, AZ • On-site

Other

Posted 7 days ago


Job description

Job Title: Junior AI Engineer

Location: Phoenix, AZ

Job Type: Only W2

Job Description:

·       Develop and deploy AI/ML models for business applications.

·       Build Generative AI and LLM-based applications using models such as GPT, Claude, Gemini, or open-source LLMs.

·       Develop RAG (Retrieval-Augmented Generation) pipelines using embeddings and vector databases.

·       Design AI agents, intelligent automation, and AI-powered workflows.

·       Prepare, clean, transform, and analyze datasets for model development.

·       Develop APIs and backend services for AI applications.

·       Fine-tune, evaluate, and optimize machine-learning/LLM models when appropriate.

·       Implement model monitoring, evaluation, security, and performance optimization.

·       Deploy AI applications using AWS, Azure, or Google Cloud.

·       Use Docker, Kubernetes, CI/CD, and MLOps practices for production deployment.

·       Collaborate with software engineers, data scientists, product managers, and business stakeholders.

·       Troubleshoot model, application, data, and production issues.

Technical Skills:

·       Python — strong proficiency

·       Machine Learning and Deep Learning

·       Generative AI / LLMs

·       Prompt engineering and LLM evaluation

·       RAG and vector databases

·       REST APIs / FastAPI

·       SQL and database fundamentals

·       Git/GitHub

·       Linux

·       Docker

·       Cloud platforms: AWS / Azure / Google Cloud Platform

·       Familiarity with PyTorch or TensorFlow

·       Understanding of NLP and/or computer vision

·       Basic MLOps and CI/CD concepts