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Junior Aws Machine Learning Jobs in Pewaukee, WI

Deloitte

Forward Deployed Engineer- AWS

Milwaukee, WI

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

Generac

Principal Data & AI Architect

Waukesha, WI

Relevant certifications such as AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate. Work Experience

AgSource

Senior AI/ML Engineer

Watertown, WI · On-site +1

$99K - $136K/yr

Experience selecting and applying techniques across statistics, operations research, machine ... Experience with deep learning frameworks and cloud-based AI services. * Experience with AWS and/or ...

Deloitte

Lead Forward Deployed Engineer - AWS

Milwaukee, WI

$101K - $133K/yr

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

Deloitte

Senior Forward Deployed Engineer- AWS

Milwaukee, WI

$103K - $141K/yr

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

Pwc

CTIO AI Engineering Manager

Milwaukee, WI · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... junior staff while upholding remarkable standards of quality and innovation in deliverables.

Generac

Senior Platform & Analytics Analyst

Waukesha, WI · On-site

$86K - $108K/yr

Additional duties include leading advanced analytics projects with machine learning and AI for ... Expertise in analytics platforms and tools (e.g., Power BI, AWS/GCP/Azure cloud services ...

Pwc

AI Engineer

Milwaukee, WI · On-site

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials - Building ...

Techvilla Solutions

ML Ops Engineer

Hales Corners, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with AWS, Azure, or GCP. * Strong knowledge of Docker and Kubernetes. * Experience with ...

New

Techvilla Solutions

ML Ops Engineer

Wauwatosa, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with AWS, Azure, or GCP. * Strong knowledge of Docker and Kubernetes. * Experience with ...

New

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All JobsAws Machine Learning JobsJunior Aws Machine Learning JobsJunior Aws Machine Learning Jobs in Pewaukee, WI

Junior Aws Machine Learning information

See Pewaukee, WI salary details

$45.5K

$92.5K

$139K

How much do junior aws machine learning jobs pay per year?

As of Aug 23, 2026, the average yearly pay for junior aws machine learning in Pewaukee, WI is $92,514.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,400.00 and $93,500.00 per year, depending on experience, location, and employer.

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 cities near Pewaukee, WI are hiring for Junior Aws Machine Learning jobs?

Cities near Pewaukee, WI with the most Junior Aws Machine Learning job openings:

Junior Aws Machine Learning jobs near you
Infographic showing various Junior Aws Machine Learning job openings in Pewaukee, WI as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $92,514 per year, or $44.5 per hour.

Applied Machine Learning Engineer II - Advanced Engineering & Technology

Milwaukee Tool

Brookfield, WI • On-site

Full-time

Re-posted 16 days ago

Job description

Job Summary:
Milwaukee Tool is a company that values innovation and culture, seeking to create disruptive technologies and solutions. The Applied Machine Learning Engineer II will utilize their machine learning expertise to deliver innovative technologies and accelerate product development in the Power Tool Accessories business unit.
Responsibilities:
• Research and evaluate emerging AI and ML technologies, advancing them through the Technology Readiness Level (TRL) process from concept through technology integration.
• Frame engineering problems as ML problems by assessing ML value versus physics‑based or analytical approaches and defining practical success criteria.
• Design, train, evaluate, and deploy ML models to solve applied science and engineering problems that expand product development capabilities.
• Build end‑to‑end ML workflows spanning data acquisition, feature engineering, model development, validation, and deployment (PyTorch, TensorFlow, CUDA, Azure ML).
• Deploy ML enabled systems on edge hardware and cloud infrastructure to support engineering decisions.
• Prepare technology transfer packages by documenting architecture decisions, known limitations, data requirements, and deployment specifications to enable technology adoption.
• Collaborate with cross-functional teams to deliver ML solutions aligned with engineering needs.
• Identify and assess emerging technologies via literature, universities, conferences, and vendor engagement.
Qualifications:
Required:
• BS in Mechanical Engineering, Electrical Engineering, Materials Science, Physics, Computer Science, Data Science, or related engineering discipline, with advanced coursework or experience in Machine Learning.
• +3 or more years of experience applying ML to physical-world engineering or scientific problems (materials, mechanical systems, manufacturing, sensor systems, chemical processes, or similar).
• Demonstrated experience designing, training, evaluating, and deploying ML models on real-world problems.
• Strong working knowledge of Python and the scientific computing ecosystem (NumPy, SciPy, Pandas, scikit‑learn), with working knowledge of SQL.
• Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow) and familiarity with cloud ML platforms (Azure ML, AWS SageMaker, or equivalent).
• Strong mathematical foundations in linear algebra, probability, statistics, and optimization, with the ability to reason about loss functions, convergence behavior, and model assumptions.
• Demonstrated ability to formulate ambiguous engineering or scientific problems into well-defined ML problems with clear objectives and evaluation criteria.
• Curiosity‑driven approach to learning new technologies and methods, with emphasis on applying machine learning to real‑world scientific and engineering challenges.
• Ability to work across a diverse range of data types.
• Hands-on approach to collaboration and evaluation of technologies.
• Ability to thrive in an ambiguous and fast-paced environment, where problem definitions evolve.
• Ability to travel 10% of the time (domestic and international).
Preferred:
• Master’s Degree or PhD in relevant field.
• Familiarity with physics-informed ML approaches, embedding physical constraints in model architecture, or surrogate modeling for simulation acceleration.
• Experience with computer vision for engineering applications.
• Exposure to edge deployment: model optimization containerized deployment to industrial hardware.
• Experience with design of experiments (DOE), uncertainty quantification, or Bayesian optimization.
• Familiarity with version control, experiment tracking, and reproducible research practices.
Company:
Milwaukee Tool manufactures electric power tools and accessories. It is a sub-organization of Techtronic Industries. Founded in 1924, the company is headquartered in Brookfield, USA, with a team of 5001-10000 employees. The company is currently .

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