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Mlops Jobs in Rochester, MI (NOW HIRING)

FTE Factory Advisors

AI Systems Engineer

Detroit, MI · On-site

Conceptual understanding of MLOps, monitoring, and operational reliability practices * Ability to operate without clean APIs or ideal data * Comfort collaborating ad communicating with non-technical ...

Deloitte

AI Engineer Senior Consultant

Detroit, MI · Hybrid

$103K - $142K/yr

Contribute to MLOps/LLMOps and production operations (versioning, reproducibility, CI/CD, automated testing, observability, incident response); support design reviews, deployment readiness, and ...

Deloitte

AI Data Engineer - Senior Consultant

Detroit, MI · Hybrid

$103K - $142K/yr

Contribute to MLOps/LLMOps and production operations (versioning, reproducibility, CI/CD, automated testing, observability, incident response); support design reviews, deployment readiness, and ...

Stellantis

Control Tower AI Lead

Auburn Hills, MI · On-site

Familiarity with cloud platforms and MLOps practices * Background in field quality analytics or customer satisfaction metrics * Prior leadership or mentoring experience in technical teams

Programmers.io

Senior Databricks Architect

Detroit, MI · On-site

$64 - $84.25/hr

Contribute to building MLOps and advanced operations frameworks. Required Qualifications * 14+ years in Data Engineering/Architecture with at least 4+ years hands-on Databricks experience delivering ...

Stellantis

Control Tower AI Lead

Auburn Hills, MI · On-site

$27.25 - $33/hr

... MLOps practices • Background in field quality analytics or customer satisfaction metrics • Prior leadership or mentoring experience in technical teams Company : Our storied and iconic brands ...

Stellantis

Data Scientist

Auburn Hills, MI · On-site

Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines * Strong grasp of machine learning algorithms like: * Regression (linear, logistic) * Causal ...

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Mlops information

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

What are the key skills and qualifications needed to thrive as an MLOps Engineer, and why are they important?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

Is MLOps a good career path?

MLOps is a growing field that combines machine learning, software engineering, and operations to deploy and maintain AI models efficiently. It offers high demand for skills in cloud platforms, automation, and data management, making it a promising career choice for those interested in AI infrastructure. Professionals in MLOps often work with tools like Docker, Kubernetes, and CI/CD pipelines, and typically require a strong understanding of both machine learning and software development.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What engineers make $500,000?

Senior machine learning operations (MLOps) engineers with extensive experience, specialized skills in cloud platforms, automation, and deployment often reach or exceed $500,000 annually in total compensation. High-level roles in tech companies or those with leadership responsibilities and advanced certifications tend to offer such salaries.

Which 3 jobs will survive AI?

For MLOps professionals, roles such as data scientists, machine learning engineers, and AI infrastructure engineers are expected to persist as AI adoption grows. These jobs require specialized skills in model development, deployment, and maintenance that complement automation. Continuous learning and expertise in tools like Kubernetes, cloud platforms, and version control are essential for long-term viability.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI director, often requiring advanced skills in data science, deep learning, and cloud platforms. These roles usually involve leadership, strategic planning, and extensive experience, and they may include bonuses or stock options that contribute to the total compensation. Such salaries are rare and generally found in large tech companies or specialized AI firms.

What are MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.
What are popular job titles related to Mlops jobs in Rochester, MI? For Mlops jobs in Rochester, MI, the most frequently searched job titles are:
What cities near Rochester, MI are hiring for Mlops jobs? Cities near Rochester, MI with the most Mlops job openings:
Mlops jobs near you

AI Systems Engineer

FTE Factory Advisors

Detroit, MI • On-site

Full-time

Posted 26 days ago

Job description

Export Control & Compliance Notice:
Because this AI Systems Engineering role directly supports data and systems subject to the International Traffic in Arms Regulations (ITAR) and Export Administration Regulations (EAR), candidate eligibility is strictly governed by US federal law. Applicants must qualify as a "U.S. Person" (U.S. Citizen, Lawful Permanent Resident/Green Card Holder, Asylee, or Refugee).

We cannot provide or transfer visa sponsorship for this position. Candidates holding temporary student or work visas (F-1 OPT/CPT, H-1B, TN, etc.) are strictly ineligible for this role due to these federal compliance constraints. [

Are you the kind of engineer who wants to see your work deployed, trusted, and used in real customer environments-not left in notebooks or demos? Do you enjoy designing the systems behind AI agents, RAG applications, and data pipelines that run in real environments with data, security, and reliability constraints? If you're energized by architecture, data engineering, and modern AI delivery, this role is for you. 

About Us 

FTE Factory Advisors helps manufacturing and operational organizations unlock higher performance - boosting revenue, reducing costs, and driving compliance through people-first strategies. Our clients are decision-makers at the top of their industries, and they trust us to deliver lasting impact. 

The Opportunity 

We're seeking an AI Systems Engineerto design and build the technical foundation behind our AI-enabled solutions on customer sites. This role sits at the intersection of software engineering, data engineering, and AI architecture. You'll be responsible for turning business objectives into AI solutions that operate in production environments. 

You'll serve as the technical counterpart to FTE's manufacturing and operations experts-bringing structure, rigor, and execution discipline to AI-enabled solutions in real customer environments. You'll own core architecture decisions, lead hands-on implementation, and develop reusable patterns as our AI delivery scales across clients and use cases. 

Requirements

What You Will Do 

  • Serve as the technical lead for on-site AI delivery, owning solutions from concept through production deployment, ensuring they are trusted by stakeholders and designed for reuse across future engagements 
  • Work on-site with manufacturing leaders, engineers, and operators to observe processes, and translate ambiguous business requirements into clear technical designs 
  • Own the design and implementation of AI agents and workflows that solve real business problems and provide measurable impact 
  • Establish prompt, retrieval, and orchestration components for AI systems 
  • Integrate AI solutions with customer applications, APIs, and structured/unstructured data sources 
  • Partner with Network & Security teams to design secure data access, identity, and information retrieval architectures 
  • Implement monitoring, logging, evaluation, and reliability controls to ensure production readiness 
  • Support internal teams by mentoring, reviewing designs, and raising the overall technical bar 

Essential Requirements 

  • 2+ Years of software engineering fundamentalswith a bias toward clean, maintainable code (Languages such as Python, Java, R, C#, or equivalent) 
  • Experience with AI agent frameworks, RAG architectures, and orchestration platforms (e.g., LangGraph, Haystack, or equivalent platforms) 
  • Understanding of data modeling, data access patterns, and system integration,including hands-on experience working with enterprise relational databases and APIs (e.g., Oracle, MySQL, Microsoft SQL Server, or equivalent relational systems) 
  • Proven ability to design for scalability, reliability, security, and long-term maintainability 
  • Conceptual understanding of MLOps, monitoring, and operational reliability practices 
  • Ability to operate without clean APIs or ideal data 
  • Comfort collaborating ad communicating with non-technical stakeholders and explaining technical information clearly and concisely to stakeholders. 
  • Strong problem-solving ability, including diagnosing system-level issues, working through incomplete or messy data, and making sound architectural tradeoffs under real-world constraints. 
  • Self-directed, pragmatic, and focused on delivering high-quality working systems-not just ideas. 

Bonus Points If You Have 

  • Demonstrated ability to design, build, and troubleshootcomplex systems including data pipelines, APIs, distributed systems, or platform services in real-world environments 
  • Experience working in industrial, operational, or highly regulated environments 
  • Experience integrating solutions into existing ("brownfield") enterprise or operational environments, including legacy systems, data sources, and vendor-managed platforms 
  • Practical experience with MLOps, system observability, or reliability engineering in production environments 
  • Experience with Cloud, NoSQL Databases, and Microsoft Dataverse. 
  • Experience with enterprise and cloud native orchestration platforms (e.g., AWS Bedrock AgentCore, Microsoft Power Automate, Google Cloud Vertex, or other equivalent cloud-native platforms) 
  • Understanding of Model Context Protocol (MCP) and/or Agent-to-Agent (A2A) emerging standards 
  • Background designing secure enterprise data access patterns 
  • Experience in consulting, enterprise systems, or production AI environments 
  • Experience standardizing platforms across multiple clients or teams 

Why Join FTE Performance 

  • Build AI systems that are deployed, used, and relied on-not shelved 
  • Work directly with senior leaders, operators, and domain experts 
  • Help shape the firm's AI architecture and technical direction from the ground up 
  • Join a growing, high-performance team that values outcomes over bureaucracy 
  • Competitive compensation, benefits, and meaningful influence on what gets built 

Benefits

We are committed to diversity and inclusion and offer a competitive wage and benefits package.

Apply