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Remote Aws Machine Learning Jobs in Michigan (NOW HIRING)

Canopy

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

Proficiency in Unix-based environments (Linux, macOS) including working with remote servers and ... Experience using cloud computing platforms, e.g., AWS or GCP. * Experience with MATLAB for ...

Autodesk

Senior Machine Learning Test Engineer

Novi, MI · On-site +1

$103.70K - $134.60K/yr

Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Experience with cloud providers (e.g., AWS, Azure, Google Cloud Platform) * Experience testing ML ...

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Remote Aws Machine Learning information

What are the key skills and qualifications needed to thrive as a Remote AWS Machine Learning Engineer, and why are they important?

To thrive as a Remote AWS Machine Learning Engineer, you need a strong background in machine learning algorithms, statistical analysis, and proficiency in programming languages such as Python, often supported by a relevant degree or certification. Familiarity with AWS services like SageMaker, Lambda, and EC2, as well as experience using cloud-based ML tools and AWS Certified Machine Learning credentials, is typically required. Excellent problem-solving skills, self-motivation, and clear written communication are valuable soft skills for remote collaboration and project management. These skills ensure effective model development, seamless deployment on cloud infrastructure, and successful remote teamwork in delivering scalable ML solutions.

What are some common challenges faced by remote AWS Machine Learning engineers, and how can they be addressed?

Remote AWS Machine Learning engineers often face challenges related to communication and collaboration, especially when working across different time zones and with cross-functional teams. Ensuring secure access to data and cloud resources is another key concern, given the sensitive nature of many machine learning projects. To overcome these challenges, engineers should leverage AWS collaboration tools, maintain clear documentation, and participate in regular virtual meetings. Additionally, setting up robust security protocols and using AWS Identity and Access Management (IAM) helps safeguard project assets while enabling effective teamwork.

What are remote AWS Machine Learning jobs?

Remote AWS Machine Learning jobs involve working with Amazon Web Services' suite of machine learning tools and services, such as SageMaker, to build, train, and deploy machine learning models. These positions allow professionals to work from anywhere, collaborating with teams virtually while leveraging AWS infrastructure to solve data-driven problems. Responsibilities often include data preprocessing, model development, and deploying scalable solutions in the cloud. Typical job titles may include Machine Learning Engineer, Data Scientist, or AI Developer, all with a focus on AWS technologies. These roles require strong programming skills, experience with cloud computing, and a background in machine learning or data science.

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

AspectRemote Aws Machine LearningRemote Data Scientist
Required CredentialsAWS certifications, machine learning coursesStatistics, data analysis, programming skills
Work EnvironmentCloud platforms, AWS services, remote teamsData analysis, modeling, research in remote settings
Industry UsageTech, finance, healthcare using AWS ML toolsResearch, consulting, analytics across industries

Remote AWS Machine Learning specialists focus on deploying machine learning models using AWS cloud services, requiring AWS certifications and cloud expertise. Remote Data Scientists analyze data, build models, and interpret results, often with a stronger emphasis on statistics and programming. While both roles work remotely and involve data, AWS Machine Learning roles are more cloud and deployment-oriented, whereas Data Scientists focus on data analysis and research.

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Remote Aws Machine Learning jobs near you
Principal Machine Learning

Principal Machine Learning

AAA Life Insurance Company

Livonia, MI • On-site, Remote

Full-time

Posted 18 days ago

Job description

Overview
General Purpose
At AAA Life, we are building a future-focused team using AI and automation to transform life insurance operations. If you're driven by meaningful work and want to deliver solutions that matter to millions of members, this is your opportunity.
We are seeking a Principal Machine Learning Engineer to serve as a technical leader within our Automation and AI organization. This role is accountable for defining and driving AI strategy, architecture, and delivery across multiple high-impact enterprise initiatives. The Principal MLE will lead the development of production-grade AI and agentic systems, ensuring successful deployment of business-critical solutions across Claims, Underwriting, and Member Services. These systems directly impact operational efficiency, decision quality, and customer experience at scale. This role requires deep expertise in modern AI, particularly in designing and deploying autonomous, agentic systems, an emerging and highly specialized area with a limited talent pool. This is a hands-on technical leadership role responsible for delivering enterprise-scale AI solutions where architectural decisions, system reliability, and model behavior have direct and measurable business impact.
Responsibilities
Position Responsibilities
  • Establish engineering standards, best practices, and evaluation frameworks for AI systems
  • Lead technical decision-making for model selection, system design, and deployment strategies
  • Act as the subject matter expert for agentic AI and modern LLM-based systems within the organization
  • Architect and deliver production-grade, multi-step AI agents capable of autonomous reasoning, tool orchestration, task decomposition, memory management, and human-in-the-loop escalation-requiring specialized expertise in emerging agentic AI frameworks
  • Design and deliver AI systems on enterprise cloud platforms (e.g., AWS, Azure), including LLM services (AWS Bedrock, Azure OpenAI), supporting high-volume, business-critical workflows with strict requirements for reliability, auditability, and performance
  • Own the agent evaluation and observability stack, including benchmarking, tracing, regression testing, and performance monitoring
  • Optimize LLM inference costs and resource utilization for production workloads
  • Partner with business leaders to identify, prioritize, and shape AI-driven initiatives aligned with organizational goals
  • Translate complex business problems into scalable AI solutions with measurable impact
  • Drive roadmap planning and investment decisions related to AI and automation
  • Collaborate with IT, data engineering, and operations teams to integrate AI solutions into enterprise systems
  • Mentor and develop machine learning engineers and data scientists
  • Provide technical guidance and elevate team capabilities in modern AI practices
  • Ensure responsible and compliant use of AI systems, including managing risks related to model behavior, data usage, and regulatory considerations in a highly regulated industry
  • Lead evaluation and integration of external AI platforms and vendors, including assessment of cost, intellectual property, scalability, security, and long-term architectural impact

Core Competencies
  • Excellent communication skills and ability to explain ML results to non-technical audiences
  • Proven ability to operate with a high degree of autonomy and accountability
  • Experience driving adoption of AI solutions in enterprise environments
  • Ability to influence technical direction and investment decisions across organizational boundaries
  • Track record of building engineering culture and raising the technical bar within a team

Qualifications
Education/Experience
  • Master's degree (or higher) in Computer Science, Engineering, Statistics, or related quantitative field
  • 10+ years of hands-on experience in machine learning, AI, or related disciplines
  • 2+ years of recent experience architecting and delivering LLM-based and agentic AI systems in production
  • Proven track record of delivering end-to-end AI solutions, from problem definition through production deployment
  • Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow)

Preferred Qualifications
  • Experience building agentic systems for document-heavy workflows (e.g., claims, underwriting, policy processing)
  • Experience with enterprise cloud AI platforms (AWS Bedrock, SageMaker, Azure OpenAI)
  • Experience with agent frameworks (LangGraph, LangChain, AutoGen, CrewAI, or equivalent)
  • Experience with AI observability and evaluation tools (e.g., Langfuse, LangSmith, or similar)
  • Familiarity with Model Context Protocol (MCP) or equivalent tool-integration standards
  • Experience deploying AI systems in regulated environments (insurance, finance, healthcare)
  • Experience leading AI architecture across multiple teams or domains

Essential Job Functions
While performing the duties of this job, the employee is frequently required to stand, walk, sit, use hands to finger, handle, or feel and talk or hear. Specific vision abilities required by this job include close vision, distance vision, color vision, depth perception, and ability to adjust focus.
This job requires the ability to perform duties contained in the job description for this position, including, but not limited to, the above requirements. Reasonable accommodations will be made for otherwise qualified applicants as needed to enable them to fulfill these requirements.
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