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Junior Aws Machine Learning Jobs in Arizona (NOW HIRING)

Lorven Technologies

AI Solution Architect

Tempe, AZ · On-site

$60.25 - $79.50/hr

AWS Machine Learning Specialty * Terraform Associate * Experience in regulated industries. * Experience designing AI systems under compliance constraints. Core Competencies * Strategic Architecture ...

Deloitte

Forward Deployed Engineer- AWS

Tempe, AZ · On-site

... 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 ...

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Google Cloud Professional Machine Learning Engineer Google Cloud Professional Data Engineer AWS Certified Machine Learning Specialty Certified Kubernetes Admin(CKA) Google Professional Cloud ...

Sarian, Inc.

Snowflake Architect

Scottsdale, AZ · On-site

$64.25 - $82.50/hr

Provide technical guidance and mentorship to junior data engineers, ensuring best practices are ... Machine Learning: * Strong experience creating and tuning machine learning models in Azure and ...

Connexzia

Data Analyst

Scottsdale, AZ · On-site

Experience with machine learning, predictive analytics, or AI-driven analytics. * Familiarity with cloud platforms such as AWS, Google Cloud, or Azure. * Experience in a specific industry (e.g ...

Deloitte

Lead Forward Deployed Engineer - AWS

Tempe, AZ · On-site

$98K - $129K/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 ...

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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.

Are there entry level AWS jobs?

Yes, there are entry-level AWS jobs such as Junior AWS Machine Learning roles that typically require foundational knowledge of cloud computing, basic understanding of machine learning concepts, and familiarity with AWS services like S3, EC2, and SageMaker. These roles often serve as starting points for careers in cloud and machine learning fields and may require certifications like AWS Certified Cloud Practitioner or AWS Certified Machine Learning – Specialty. Candidates should be prepared to learn on the job and develop skills through training and hands-on experience.

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:
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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 Aws Machine Learning jobs near you

AI Solution Architect

Lorven Technologies

Tempe, AZ • On-site

$60.25 - $79.50/hr

Contractor

Re-posted 25 days ago

Job description

AI Solution Architect - Agentic & Generative AI
Locations: Austin, Tx | Tempe, Az | Charlotte, NC | New York, NY - Onsite
Duration: 12 Months Contract
Role Overview
The AI Solution Architect is a senior technical leader responsible for designing, architecting, and operationalizing Agentic AI and Generative AI solutions at enterprise scale. This role is central to shaping and implementing an AI-first Product Delivery Lifecycle (PDLC), ensuring product development processes, engineering practices, and operating models are optimized for AI-native platforms, agentic systems, and rapid value iteration.
This individual will operate at the intersection of architecture, AI platform engineering, ML lifecycle management, enterprise integration, governance, and organizational transformation.
Key Responsibilities
AI Architecture & Solution Design
  • Architect enterprise-scale Agentic and Generative AI systems, including:
  • Multi-agent orchestration frameworks
  • Retrieval-Augmented Generation (RAG) pipelines
  • Autonomous task execution patterns
  • Tool-use integration frameworks
  • Establish reference architectures for an AI-first PDLC covering:
  • Design
  • Development
  • Testing & evaluation
  • Deployment
  • Monitoring & observability
  • Risk controls & governance
  • Design and implement model lifecycle pipelines including:
  • Training & fine-tuning workflows
  • Evaluation harnesses
  • Model registry & versioning
  • Continuous improvement loops
  • Embed AI capabilities into client-facing platforms, internal tooling, and operational workflows.

AI-First PDLC Transformation
  • Define architecture, tooling, and standards for an AI-native delivery lifecycle, including:
  • Prompt engineering frameworks
  • Agent design patterns
  • Automated LLM evaluation systems
  • Safety guardrails and policy enforcement
  • Data quality validation mechanisms
  • Integrate AI evaluation and governance gates into CI/CD pipelines.
  • Establish best practices to enable cross-functional teams to become AI "builders."
  • Drive adoption of AI-driven development patterns across product, engineering, design, and risk functions.

Enterprise Integration & Data Strategy
  • Design integrations between AI systems and core enterprise platforms.
  • Partner with Data Engineering teams to define:
  • Data ingestion architectures
  • Embedding & vectorization strategies
  • Feature stores
  • Real-time inference pipelines
  • Ensure architectural alignment with:
  • Cloud strategy
  • Enterprise data governance
  • Security & compliance standards

Security, Compliance & Responsible AI
  • Architect AI systems in compliance with regulatory and governance requirements.
  • Embed identity, authorization, auditing, and model-level security patterns.
  • Implement Responsible AI practices, including:
  • Transparency
  • Bias monitoring
  • Fairness evaluation
  • Performance & drift monitoring
  • Auditability

Cross-Functional Leadership
  • Partner with senior leaders across Product, Enterprise Architecture, DevSecOps, Infrastructure, and Operations.
  • Lead architectural reviews and design whiteboarding sessions.
  • Mentor engineering teams and contribute to AI architecture standards.
  • Support hiring and talent development for emerging AI roles.

Required Qualifications
  • 2+ years architecting Generative AI, Agentic AI, or ML systems at enterprise scale.
  • 6+ years experience in cloud-native architecture (AWS preferred), including:
  • Microservices
  • Kubernetes
  • Event-driven systems
  • 5+ years hands-on experience with:
  • LLMs
  • Vector databases
  • Embeddings
  • Evaluation frameworks
  • Guardrails
  • Fine-tuning
  • Orchestration frameworks
  • 8+ years experience in Python.
  • Proficiency in C#, Java, or TypeScript is a plus.
  • 6+ years experience in ML Ops, including:
  • CI/CD for ML
  • Model versioning
  • Monitoring
  • Automated evaluation

Preferred Qualifications
  • Bachelor's degree in Computer Science, Engineering, AI/ML, or equivalent experience.
  • Relevant certifications such as:
  • AWS Solutions Architect - Professional
  • AWS Machine Learning Specialty
  • Terraform Associate
  • Experience in regulated industries.
  • Experience designing AI systems under compliance constraints.

Core Competencies
  • Strategic Architecture & Systems Thinking
  • AI Fluency & Model Lifecycle Expertise
  • Experimentation & Data-Driven Decision-Making
  • Cross-Functional Collaboration
  • Innovation & Continuous Learning
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About Lorven technologies

Sourced by ZipRecruiter

Lorven Technologies, headquartered in Plainsboro, New Jersey, United States, is a reputable company in the technology industry, specializing in providing effective IT solutions and consulting services. The company's official website, lorventech.com, offers comprehensive insights into its offerings which include but are not limited to software development, IT consulting, project management, and business analysis. Since its inception, Lorven Technologies has been committed to ensuring efficiency and reliability in delivering IT services to its global clientele, establishing itself as a trusted name in the industry.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Plainsboro, NJ, US

Year founded

2001

Social media

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