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Work Based Learning Program Aws Jobs in Dallas, TX

Concentra

Engineer II, AWS Connect

Addison, TX

By developing robust cloud-based contactcenter solutions, this position directly contributes to ... Colleague Referral Bonus Program * Tuition Reimbursement * Commuter Benefits * Dependent Care ...

Concentra

Engineer II, AWS Connect

Addison, TX

By developing robust cloud-based contactcenter solutions, this position directly contributes to ... Colleague Referral Bonus Program * Tuition Reimbursement * Commuter Benefits * Dependent Care ...

Concentra

Engineer II, AWS Connect

Addison, TX

By developing robust cloud-based contactcenter solutions, this position directly contributes to ... Colleague Referral Bonus Program * Tuition Reimbursement * Commuter Benefits * Dependent Care ...

Stride, Inc.

Computer Science CTE Teacher

Denton, TX · Remote

$49K/yr

Supports CTE Program Coordinator/school/program with creating business and industry partnerships to help navigate work-based learning experiences for students * Supports a project-based learning ...

Sonsoft Inc

AWS Architect

Dallas, TX

$64 - $84/hr

Company Description Sonsoft , Inc. is a USA based corporation duly organized under the laws of the ... U.S. Citizens and those who are authorized to work independently in the United States are ...

Stride, Inc.

CRE Cybersecurity Teacher

Denton, TX · Remote

$49K/yr

Supports CTE Program Coordinator/school/program with creating business and industry partnerships to help navigate work-based learning experiences for students * Supports a project-based learning ...

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All JobsWork Based Learning Program Aws JobsWork Based Learning Program Aws Jobs in Dallas, TX

Work Based Learning Program Aws information

See Dallas, TX salary details

$46.5K

$80.5K

$181.5K

How much do work based learning program aws jobs pay per year?

As of Jul 19, 2026, the average yearly pay for work based learning program aws in Dallas, TX is $80,492.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,400.00 and $88,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in a Work-Based Learning Program focused on AWS, and why are they important?

To thrive in a Work-Based Learning Program focused on AWS, you need foundational knowledge of cloud computing concepts, basic programming skills, and familiarity with networking, supported by relevant coursework or entry-level certifications like AWS Cloud Practitioner. Hands-on experience with AWS tools such as EC2, S3, Lambda, and the AWS Management Console is typically required, along with understanding of version control systems like Git. Strong problem-solving abilities, willingness to learn, and effective communication are important soft skills for adapting to real-world technical environments. These skills and qualities are crucial for successfully applying cloud concepts in practical settings and collaborating with teams to solve business challenges.

What is the difference between Work Based Learning Program Aws vs Cloud Support Associate?

AspectWork Based Learning Program AwsCloud Support Associate
CredentialsTypically no formal certifications required; focus on trainingOften requires AWS certifications or related cloud credentials
Work EnvironmentEducational or training setting, often part-time or internshipProfessional cloud support environment, full-time role
Employer & Industry UsageEducational institutions, training providers, AWS programsCloud service providers, IT companies, AWS partners

The Work Based Learning Program Aws is primarily a training or internship opportunity designed to develop skills in AWS cloud services, often without requiring prior certifications. In contrast, a Cloud Support Associate is a full-time professional role that typically requires AWS certifications and involves supporting cloud customers in a real-world environment. While the learning program focuses on education and skill development, the associate role emphasizes practical support and troubleshooting in the industry.

What is a Work Based Learning Program with AWS?

A Work Based Learning Program with AWS is an educational initiative that combines classroom instruction with real-world work experience using Amazon Web Services (AWS) technologies. These programs are designed to help students and professionals gain hands-on cloud computing skills by working on projects, internships, or apprenticeships in partnership with employers. Participants learn about cloud infrastructure, deployment, and AWS services, making them more competitive in the job market. Such programs often include mentorship, industry certifications, and exposure to real business challenges.

How does participating in an AWS Work-Based Learning Program help prepare candidates for a cloud-focused career?

Participating in an AWS Work-Based Learning Program offers hands-on experience with key AWS cloud services, allowing candidates to apply classroom concepts to real-world projects. You'll typically collaborate with mentors and teammates in a structured environment, working on tasks such as cloud migration, automation, and security configuration. This immersion helps build both technical and professional skills, making you more competitive for roles such as cloud support associate or solutions architect. Additionally, exposure to industry best practices and networking opportunities within the program can significantly accelerate your career growth in cloud computing.
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Work Based Learning Program Aws jobs near you
Machine Learning Software Engineer II

Machine Learning Software Engineer II

Cambium Learning Group

Dallas, TX • On-site, Remote

$89K - $123K/yr

Full-time

Re-posted 15 days ago

Cambium Learning Group rating

9.2

Company rating: 9.2 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

19th of 209 rated software companies

Job description

Cambium Learning® Group is an award-winning educational technology solutions leader dedicated to helping all students reach their potential through individualized and differentiated instruction. Using a research-based, personalized approach, Cambium Learning Group delivers SaaS resources and instructional products that engage students and support teachers in fun, positive, safe and scalable environments. These solutions are provided through Learning A-Z® (online differentiated instruction for elementary school reading, writing and science), ExploreLearning® (online interactive math and science simulations, a math fact fluency solution, and a K-2 science solution), Voyager Sopris Learning® (blended solutions that accelerate struggling learners to achieve in literacy and math and professional development for teachers), and VKidz Learning (online comprehensive homeschool education and programs for literacy and science). We believe that every student has unlimited potential, that teachers matter, and that data, instruction, and practice are the keys to success in the classroom and beyond.
Job Overview:
We are seeking a talented Machine Learning Engineer II to join our CAI machine learning and scoring development team. In this role, you will be the crucial bridge between applied research and production systems. Working alongside a cross-functional group of mathematicians, computer scientists, psychometricians, and statisticians, you will design and deploy custom machine learning solutions for our clients and internal platforms.
The ideal candidate is a full-stack ML practitioner who is equally comfortable discussing algorithmic design with researchers and architecting scalable, low-latency production systems. You will own the full software development lifecycle-transforming research prototypes into optimized, production-ready solutions using modern AWS infrastructure such as SageMaker, ECS, and Lambda, with an emphasis on high-throughput inference and PyTorch-to-ONNX model optimization.
Job Responsibilities:
  • Full-Lifecycle ML Development: Lead the transition of machine learning models from theoretical prototypes into scalable, high-performance production systems.
  • AWS Cloud Architecture & Deployment: Architect and deploy ML solutions utilizing AWS ECS (Elastic Container Service) for containerized workloads and AWS Lambda for serverless, event-driven inference pipelines.
  • Model & Inference Optimization: Optimize PyTorch models for production deployment by converting them to ONNX formats. Apply advanced inference optimization techniques (quantization, pruning, ONNX Runtime) and memory-efficient attention mechanisms like Flash Attention to minimize latency and maximize throughput.
  • Infrastructure & Engineering Best Practices: Champion infrastructure best practices for machine learning systems, establishing reliable CI/CD pipelines, and ensuring robust, secure, and reproducible deployments across the AWS ecosystem.
  • Algorithm Engineering: Design, develop, and evaluate algorithms that generate descriptive, diagnostic, predictive, and prescriptive insights from both structured and unstructured data.
  • Robust Software Engineering: Write clean, efficient, and well-tested code. Complete rigorous testing, debugging, and documentation to ensure seamless installation and long-term maintenance.
  • Cross-Functional Collaboration: Actively participate in research discussions, requirements gathering, and system design alongside domain experts to build tailored scoring and ML solutions.

Job Requirements:
  • Experience: 2-5 years of industry experience in Machine Learning Engineering, Software Engineering, or Data Science, with a proven track record of architecting and deploying models to production.
  • Cloud & MLOps Infrastructure: Deep, hands-on experience with the AWS ecosystem, specifically AWS ECS and Lambda. Solid understanding of containerization (Docker) and event-driven architectures.
  • Programming Proficiency: Strong proficiency in modern programming languages used in ML (e.g., Python, C++, Java) and familiarity with industry-standard coding practices.
  • ML Frameworks & Advanced Optimization: Hands-on experience with PyTorch and other machine learning libraries (e.g., Scikit-Learn, TensorFlow). Deep understanding of model optimization pipelines, including PyTorch to ONNX conversions, ONNX Runtime, and scaling attention mechanisms (e.g., Flash Attention).
  • Data Systems: Experience working with large-scale computing frameworks, data analysis systems, and relational/non-relational databases.

Nice to Have's:
  • AWS SageMaker: Experience utilizing AWS SageMaker for managed model training and hosting.
  • Advanced LLMOps & Fine-Tuning: Hands-on experience applying modern parameter-efficient fine-tuning methods (such as LoRA and qLoRA) to large language models.
  • AI Agents: Experience building, integrating, and deploying autonomous or semi-autonomous AI agents to automate complex workflows and connect ML models with external tools/APIs.
  • NLP Expertise: Proven experience and familiarity with deep learning technologies applied specifically to Natural Language Processing (NLP) and complex text-based modeling.
  • Cross-Disciplinary Collaboration: Experience collaborating with specialized researchers (e.g., psychometricians, statisticians) to operationalize complex mathematical concepts.
  • Infrastructure as Code: Experience implementing IaC using tools like Terraform or AWS CloudFormation.
  • Model Monitoring: Experience setting up comprehensive model monitoring systems to detect data drift, concept drift, and model degradation in production AWS environments.

To apply for this opportunity, simply click on the "Apply" button and submit a cover letter and resume.
An Equal Opportunity Employer
We are dedicated to fostering a culture that celebrates unique backgrounds, ideas, and experiences. All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, religion, sex, gender, gender identity/expression, sexual orientation, national origin, protected veteran status, or disability.

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