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Executive Aws Machine Learning Jobs (NOW HIRING)

CCT

Machine Learning Engineer (AWS)

Tulsa, OK · On-site

Requirements * 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline. * Hands-on experience with AWS ML and data services -- SageMaker (training, endpoints ...

Georgia IT Inc

Machine Learning -Hybrid

Manhattan, NY · On-site

Communicate findings and recommendations to stakeholders and executive leadership. Stay updated ... Azure, AWS, GCP Proven track record of successfully deploying and optimizing ML models in a ...

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Executive Aws Machine Learning information

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$26.5K

$93.6K

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

As of Jun 14, 2026, the average yearly pay for executive aws machine learning in the United States is $93,552.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,000.00 and $120,500.00 per year, depending on experience, location, and employer.

What does an Executive AWS Machine Learning professional do?

An Executive AWS Machine Learning professional leads the strategic planning and implementation of machine learning solutions using Amazon Web Services (AWS) within an organization. Their responsibilities often include overseeing teams, aligning machine learning initiatives with business goals, and ensuring that scalable, secure, and cost-effective AWS-based ML services are deployed. They also stay updated on the latest AWS technologies, foster innovation, and communicate with stakeholders to drive digital transformation. This role requires a blend of technical expertise, leadership skills, and business acumen.

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

AspectExecutive Aws Machine LearningData Scientist
Required CredentialsAdvanced AWS certifications, leadership experienceStatistics, programming, data analysis degrees
Work EnvironmentLeadership roles, strategic planning, cloud infrastructureData analysis, model development, research
Employer & Industry UsageTech companies, cloud service providers, enterprisesResearch institutions, tech firms, finance, healthcare
Common Search & ComparisonYesYes

Executive Aws Machine Learning professionals focus on strategic leadership, cloud infrastructure, and overseeing ML projects within organizations, often requiring AWS certifications and leadership skills. Data Scientists primarily analyze data, develop models, and perform research to extract insights. While both roles work with machine learning, the Executive Aws Machine Learning role emphasizes management and cloud expertise, whereas Data Scientists focus on technical data analysis and model building.

What are some common challenges faced by an Executive AWS Machine Learning professional when leading cross-functional teams?

As an Executive AWS Machine Learning professional, a frequent challenge is bridging the gap between data science teams, engineering, and business stakeholders. Ensuring all teams are aligned on project goals, timelines, and technical requirements can be complex, especially when translating machine learning concepts for non-technical audiences. Additionally, managing cloud resource allocation and cost efficiency on AWS while delivering scalable ML solutions requires strategic oversight. Strong communication, project management skills, and a deep understanding of AWS services are essential to successfully lead collaborative, multidisciplinary teams.

What are the key skills and qualifications needed to thrive as an Executive AWS Machine Learning specialist, and why are they important?

To thrive as an Executive AWS Machine Learning specialist, you need deep expertise in machine learning concepts, cloud architecture, and a strong background in computer science or related fields, often supported by advanced degrees or AWS certifications. Familiarity with AWS services like SageMaker, Lambda, and data management tools, along with certifications such as AWS Certified Machine Learning – Specialty, is highly valuable. Leadership, strategic thinking, and the ability to communicate complex technical ideas to both technical and non-technical stakeholders are crucial soft skills. These skills ensure effective development and deployment of scalable ML solutions that align with business goals and drive innovation.
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Executive Aws Machine Learning jobs near you
Infographic showing various Executive Aws Machine Learning job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 98% Full Time, and 1% Part Time. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $93,552 per year, or $45 per hour.

AI AWS Technical Architect

Purple Drive Technologies

Parsippany, NJ • On-site

$65 - $85.50/hr

Full-time

Posted 20 days ago

Job description

Overview:
Job Title: AI AWS Technical Architect
Experience: 6-8 Years
Job Type: Full-Time
Job Summary
We are seeking an experienced AI AWS Technical Architect with strong expertise in designing and implementing enterprise-scale AI/ML and Generative AI solutions on AWS cloud platforms. The ideal candidate will possess hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), cloud-native AI architectures, MLOps, and scalable distributed systems.
This role requires strong technical leadership, architecture design capabilities, and hands-on engineering expertise to deliver secure, scalable, and high-performance AI-driven applications.
Required Skills
  • Strong expertise in:
    • AWS Cloud Architecture
    • Generative AI
    • Large Language Models (LLMs)
    • AI/ML Solution Design
  • Hands-on experience with:
    • Amazon Bedrock
    • SageMaker
    • AWS Lambda
    • Amazon EKS
    • API Gateway
  • Strong programming expertise in:
    • Python
    • Node.js
  • Experience with:
    • LangChain
    • AutoGen
    • LangGraph
    • Agentic AI frameworks
  • Strong understanding of:
    • RAG architectures
    • Embeddings
    • Vector databases
    • Prompt engineering
  • Experience with vector databases such as:
    • Qdrant
    • Pinecone
    • OpenSearch
    • MongoDB Atlas Vector Search
  • Expertise in:
    • CI/CD pipelines
    • MLOps
    • Kubernetes
    • Containerization
  • Strong understanding of:
    • Responsible AI
    • AI governance
    • Security best practices
    • FinOps optimization
Key Responsibilities
  • Design and architect scalable AI/ML and Generative AI solutions on AWS cloud
  • Build and implement RAG-based enterprise AI systems using LLMs and vector databases
  • Develop AI orchestration workflows and agentic AI solutions using modern AI frameworks
  • Architect scalable cloud-native AI infrastructure leveraging AWS services
  • Define embedding strategies, prompt engineering standards, and retrieval optimization techniques
  • Establish MLOps practices including:
    • CI/CD pipelines
    • Model deployment
    • Monitoring
    • Lifecycle management
  • Implement security, governance, and Responsible AI best practices
  • Optimize AI systems for:
    • Performance
    • Reliability
    • Cost efficiency
    • Scalability
  • Collaborate with engineering, DevOps, product, and business teams to drive AI initiatives
  • Mentor technical teams and provide architectural guidance across AI programs
Preferred Qualifications
  • Experience building enterprise AI platforms and distributed systems
  • AWS Certifications preferred:
    • AWS Solutions Architect
    • AWS Machine Learning Specialty
  • Exposure to Kubernetes and cloud-native deployment patterns
  • Strong communication and stakeholder management skills

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