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Aws Sagemaker Remote Jobs (NOW HIRING)

Lennar

Lead Machine Learning Engineer - REMOTE

Boston, MA · Remote

$111K - $146K/yr

They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases ... Remote work schedule, with a preference for candidates based in Miami, FL; Bentonville, AR; or ...

Lennar

Lead Machine Learning Engineer - REMOTE

Boston, MA · Remote

$111K - $146K/yr

They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases ... Remote work schedule, with a preference for candidates based in Miami, FL; Bentonville, AR; or ...

Lennar Homes

Lead Machine Learning Engineer - REMOTE

Lynn, MA · Remote

$105K - $139K/yr

They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases ... Remote work schedule, with a preference for candidates based in Miami, FL; Bentonville, AR; or ...

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How much do aws sagemaker remote jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for aws sagemaker remote in the United States is $54.05, according to ZipRecruiter salary data. Most workers in this role earn between $38.70 and $64.42 per hour, depending on experience, location, and employer.

What are some common challenges faced by AWS SageMaker professionals working remotely, and how can they be addressed?

Remote AWS SageMaker professionals often encounter challenges such as managing secure access to sensitive data, collaborating effectively with distributed teams, and ensuring consistent deployment environments. To address these, it's important to leverage AWS security best practices, use version control and documentation tools, and participate in regular virtual meetings to stay aligned with team members. Additionally, taking advantage of AWS’s integrated collaboration features and establishing clear communication protocols can help mitigate these obstacles and ensure project success.

What is an AWS SageMaker remote job?

An AWS SageMaker remote job typically refers to a position where professionals use Amazon SageMaker, a cloud-based machine learning platform, to develop, train, and deploy machine learning models while working remotely. These roles often involve collaborating with teams via online tools, performing data analysis, and building models using SageMaker's suite of features without having to be physically present in an office. This allows for flexibility and access to global talent, as all work can be conducted over the internet while leveraging AWS infrastructure.

What is the difference between Aws Sagemaker Remote vs Data Scientist?

AspectAws Sagemaker RemoteData Scientist
Required CredentialsAWS certifications, cloud computing skillsStatistics, data analysis, programming (Python/R)
Work EnvironmentCloud platforms, remote or on-premiseOffice, remote, or hybrid
Industry UsageMachine learning deployment, cloud servicesData analysis, modeling, research

While Aws Sagemaker Remote focuses on deploying and managing machine learning models on AWS cloud, Data Scientists primarily analyze data, build models, and generate insights. Both roles require technical skills, but Sagemaker Remote emphasizes cloud infrastructure and deployment, whereas Data Scientists focus on data analysis and modeling.

What are the key skills and qualifications needed to thrive as an AWS SageMaker Remote Specialist, and why are they important?

To thrive as an AWS SageMaker Remote Specialist, you need expertise in machine learning, data science, cloud computing, and a strong understanding of AWS services, often supported by a degree in computer science or a related field. Familiarity with tools like Jupyter Notebooks, Python, TensorFlow, and official AWS certifications such as AWS Certified Machine Learning – Specialty are typically required. Excellent problem-solving, teamwork, and communication skills help you collaborate with distributed teams and translate business needs into technical solutions. These competencies are crucial for efficiently building, deploying, and managing scalable machine learning models in a remote cloud environment.
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Aws Sagemaker Remote jobs near you
Infographic showing various Aws Sagemaker Remote job openings in the United States as of May 2026, with employment types broken down into 2% Locum Tenens, 96% Full Time, and 2% Temporary. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $112,422 per year, or $54 per hour.
AI Solutions Architect - Remote

AI Solutions Architect - Remote

NAVA Software Solutions

Jersey City, NJ • On-site, Remote

$69 - $90.75/hr

Full-time

Posted 6 days ago

Job description

NAVA Software solutions is looking for a AI Solutions Architect
Details:
AI Solution Architect - Insurance Domain (Azure & AWS)
Location: Remote
Duration: 12 months
Role Overview
As an AI Solution Architect specializing in Azure and AWS, you will lead the design, development, and production deployment of large-scale AI/ML solutions tailored for the insurance industry. You will work closely with cross-functional teams including data scientists, engineers, actuaries, and business leaders to transform business strategy into secure, scalable, and cost-effective AI architectures.
Key Responsibilities
AI Strategy & Use Case Development
  • Identify and prioritize AI/ML use cases across the insurance value chain: Underwriting, pricing, claims fraud detection, customer segmentation, policy recommendation engines, and chatbots.
  • Partner with business stakeholders (e.g., actuaries, underwriters, claims analysts) to define impactful AI-driven solutions that enhance decision-making and operational efficiency.
Architecture & Design
  • Design resilient, scalable, and cloud-agnostic AI/ML architectures using Azure and AWS.
  • Build and manage data ingestion and transformation pipelines using Azure Data Factory and AWS Glue.
  • Define and implement MLOps workflows using Azure ML Pipelines, AWS SageMaker Pipelines, and MLflow.
Technical Leadership
  • Lead design reviews, technical workshops, and blueprint sessions.
  • Mentor engineers and data scientists in best practices for model development, deployment, and cloud-native AI.
Solution Development
  • Implement NLP, computer vision, and deep learning solutions using Azure Cognitive Services, AWS Comprehend, Rekognition, and Bedrock.
  • Develop microservices/APIs (Python, FastAPI) for real-time inference and batch scoring.
  • Work with frameworks like TensorFlow, PyTorch, and Scikit-learn.
Integration with Insurance Systems
  • Ensure seamless integration with core insurance platforms: Policy Administration, Claims Management, Billing, CRM (e.g., Guidewire, Duck Creek, Salesforce).
  • Collaborate with enterprise architects to align AI with broader IT modernization initiatives.

Deployment & Operations
  • Containerize models using Docker, deploy via Kubernetes (AKS/EKS).
  • Implement CI/CD automation (Azure DevOps, AWS CodePipeline) and observability (CloudWatch, Prometheus, Azure Monitor).
Governance & Security
  • Enforce cloud security and data compliance using IAM, VNet, KMS, and encryption protocols.
  • Leverage Azure Responsible AI and AWS SageMaker Clarify for explainability, fairness, and auditability.
Stakeholder Engagement
  • Present technical architectures and value propositions to C-level executives, claims directors, and underwriting heads.
  • Serve as the bridge between business needs and AI/ML capabilities.
Required Qualifications
Experience:
  • 8-10 years in AI/ML and software/system architecture.
  • 5+ years in solution/technical leadership roles.

Education:
  • Bachelor's in Computer Science, Data Science, or Engineering.
  • Master's or PhD in AI/ML preferred.

Cloud Expertise:
  • Azure: Azure ML, Cognitive Services, Data Factory, Databricks, Cosmos DB
  • AWS: SageMaker, Comprehend, Rekognition, Glue, Redshift, DynamoDB

Tools & Frameworks:
  • Languages: Python (mandatory), Java or C++
  • ML Frameworks: TensorFlow, PyTorch, Scikit-learn
  • Big Data & Streaming: Spark, Kafka, Hadoop
  • MLOps/DevOps: Kubernetes (AKS/EKS), Docker, MLflow, Kubeflow, CI/CD pipelines
Preferred Qualifications
  • Certifications: Azure AI Engineer Associate, AWS Certified Machine Learning - Specialty
  • Advanced AI Expertise: Generative AI (Azure OpenAI, ChatGPT, AWS Bedrock), Prompt Engineering, Agentic AI
  • Community & Research: Contributions to open-source projects or AI/ML publications
  • Soft Skills: Strong communication, stakeholder management, and strategic thinking. Team leadership and mentoring
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About NAVA Software Solutions

Sourced by ZipRecruiter

NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

Social media

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