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Mlops Jobs in Rochester, MI (NOW HIRING)

Build and maintain ML infrastructure using modern MLOps practices and tools (e.g., MLflow, Kubeflow, Vertex AI Pipelines) * Implement model monitoring, versioning, and performance tracking systems

Deploy and operationalize models using MLOps best practices, including versioning, monitoring, performance tracking, and continuous improvement. * Prepare and deliver executive-ready presentations ...

Data Scientist 2

Southfield, MI · On-site

$90K - $113K/yr

Deploy and operationalize models using MLOps best practices, including versioning, monitoring, performance tracking, and continuous improvement. * Prepare and deliver executive-ready presentations ...

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 ...

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 ...

Senior Machine Learning Test Engineer

Novi, MI · On-site +1

$103K - $134K/yr

Your skills span test strategy, automation, and a little MLOps, with a strong software engineering base. You are excited to collaborate across research and product to ship ML capabilities with clear ...

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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:

Other

Medical, Dental, Vision, Retirement, PTO

Posted 20 days ago


Job description

Company Description

Hi there! We're Razorfish. We've been leading the marketing industry with our digital expertise since the start of the internet. But in 2020, we did a full reboot. What's different? It all starts with people. Weird, wonderful, complex people - with diverse backgrounds in strategy, creative and technology. But no matter how different we are, we all have one thing in common. We believe our differences are our strength. So we push for inclusion, challenge convention and bring in new perspectives, to inspire new ideas. Because when we connect by understanding what makes people different, we can create unforgettable experiences that enrich lives. Join us at razorfish.com.

Overview

We're seeking a Machine Learning Engineer to help design, build, and maintain production-grade ML systems across cloud platforms. This role blends software engineering and ML expertise to translate prototypes into scalable solutions. You'll own the full ML lifecycle from development and deployment to monitoring and optimization using tools like Databricks, Vertex AI, and other cloud-native platforms. Strong technical skills, collaboration, and a passion for delivering AI at scale are essential.

For this role, we expect the candidate to demonstrate a track record of: 1. Collaborating with Data Science teams to deploy ML solutions into production. 2. Hands-on MLOps experience, including model deployment, monitoring, and lifecycle management. 3. Designing data warehouses and orchestrating data pipelines to support scalable ML operations.

Responsibilities

ML System Development & Deployment

  • Design, build, and maintain scalable ML pipelines using cloud services (e.g., Vertex AI, Databricks, SageMaker, Azure ML)
  • Develop and integrate microservices, REST APIs, and webhooks for ML model serving
  • Implement CI/CD pipelines for automated model training, testing, and deployment
  • Create robust data processing workflows for model training and inference

MLOps & Infrastructure

  • Build and maintain ML infrastructure using modern MLOps practices and tools (e.g., MLflow, Kubeflow, Vertex AI Pipelines)
  • Implement model monitoring, versioning, and performance tracking systems
  • Design automated retraining pipelines and manage model lifecycle
  • Ensure reliability, scalability, and security of models in production
  • Optimize inference performance and cost efficiency across cloud platforms

Software Engineering Excellence

  • Write clean, maintainable, and well-documented code following best practices
  • Implement comprehensive testing strategies including unit, integration, and model testing
  • Contribute to technical design reviews and architecture decisions
  • Maintain high code quality standards and participate in code reviews

Cross-Functional Collaboration

  • Partner with data scientists to productionize research models and prototypes
  • Collaborate with data engineers to design efficient data pipelines and feature stores
  • Work with product teams to integrate ML capabilities into customer-facing applications
  • Participate in agile development processes and cross-functional project planning
  • Provide technical guidance and mentorship to junior team members
Qualifications

Education & Experience

  • Bachelor's degree in Computer Science, Software Engineering, Data Science, Mathematics, or related field
  • 3-4 years of professional experience in ML engineering, software engineering, or data science
  • 2+ years of hands-on experience deploying and maintaining ML models in production
  • Experience working in collaborative, cross-functional team environments

Technical Skills

  • Programming Languages: Strong proficiency in Python and SQL (2+ years)
  • ML Frameworks: Experience with XGBoost, TensorFlow, PyTorch, sklearn, or Keras
  • Cloud Platforms: Solid hands-on experience with GCP, AWS, or Azure
  • ML Platforms: Practical knowledge of Vertex AI, SageMaker, Azure ML, or Databricks
  • Analytics & Feature Engineering: Proficient with BigQuery, Redshift, Azure Synapse
  • Distributed Processing: Skilled in Databricks, Apache Spark, Dataflow, Pub/Sub, Kafka
  • Workflow Orchestration: Experience with Airflow, Cloud Composer, Jenkins
  • Networking & Security: Understanding of cloud networking, security, and cost optimization
  • MLOps & DevOps: Familiarity with CI/CD, ML lifecycle management
  • API Development: Experience with REST APIs and microservices
  • Version Control: Proficiency with Git and collaborative development workflows

Core Competencies

  • Strong understanding of ML algorithms, model evaluation, and validation
  • Experience with data preprocessing, feature engineering, and performance tuning
  • Solid software engineering fundamentals and coding best practices
  • Awareness of data privacy, security, and ethical AI principles
  • Excellent collaboration skills with technical and non-technical stakeholders
  • Self-driven learner with curiosity about emerging ML technologies

Preferred Qualifications

Advanced Technical Skills

  • MLOps Tools: MLflow, Kubeflow, Vertex AI Pipelines
  • Containerization: Docker; basic Kubernetes knowledge
  • Specialized ML: Exposure to NLP, computer vision, or deep learning
  • Modern ML: Familiarity with LLMs, RAG patterns, transformer architectures

 

Professional Experience

  • Agile development and cross-functional collaboration
  • Code review and technical documentation practices
  • Interest in mentorship and knowledge sharing
  • Experience with model validation and software testing principles
 Additional Information

The Power of One starts with our people! To do powerful things, we offer powerful resources. Our best-in-class wellness and benefits offerings include:

  • Paid Family Care for parents and caregivers for 12 weeks or more
  • Monetary assistance and support for Adoption, Surrogacy and Fertility
  • Monetary assistance and support for pet adoption
  • Employee Assistance Programs and Health/Wellness/Comfort reimbursements to help you invest in your future and work/life balance
  • Tuition Assistance
  • Paid time off that includes Flexible Time off Vacation, Annual Sick Days, Volunteer Days, Holiday and Identity days, and more
  • Matching Gifts programs
  • Flexible working arrangements
  • 'Work Your World' Program encouraging employees to work from anywhere Publicis Groupe has an office for up to 6 weeks a year (based upon eligibility)
  • Business Resource Groups that support multiple affinities and alliances

The benefits offerings listed are available to eligible U.S. Based employees, are reviewed on an annual basis, and are governed by the terms of the applicable plan documents.

Razorfish is an Equal Opportunity Employer. Our employment decisions are made without regard to actual or perceived race, color, ethnicity, religion, creed, sex, sexual orientation, gender, gender identity, gender expression, pregnancy, childbirth and related medical conditions, national origin, ancestry, citizenship status, age, disability, medical condition as defined by applicable state law, genetic information, marital status, military service and veteran status, or any other characteristic protected by applicable federal, state or local laws and ordinances.

If you require accommodation or assistance with the application or onboarding process specifically, please contact USMSTACompliance@publicis.com.

All your information will be kept confidential according to EEO guidelines.

Compensation Range: $87,210 to $119,300. This is the pay range the Company believes it will pay for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time. Temporary roles may be eligible to participate in our freelancer/temporary employee medical plan through a third-party benefits administration system once certain criteria have been met. Temporary roles may also qualify for participation in our 401(k) plan after eligibility criteria have been met. For regular roles, the Company will offer medical coverage, dental, vision, disability, 401k, and paid time off. The Company anticipates the application deadline for this job posting will be 9/1/25.

 Compensation Range: USD $87,210.00 - USD $119,300.00/Annually. This is the pay range the Company believes it will pay for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time. Temporary roles may be eligible to participate in our freelancer/temporary employee medical plan through a third-party benefits administration system once certain criteria have been met. Temporary roles may also qualify for participation in our 401(k) plan after eligibility criteria have been met. For regular roles, the Company will offer medical coverage, dental, vision, disability, 401k, and paid time off. The Company anticipates the application deadline for this job posting will be 11/15/2025.Employment Type: OTHER