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Mlops Machine Learning Engineer Jobs in Michigan

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Senior Machine Learning Test Engineer

Novi, MI · On-site +1

$103K - $134K/yr

Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Your skills span test strategy, automation, and a little MLOps, with a strong software engineering ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Showing results 41-60

Mlops Machine Learning Engineer information

What does an MLOps machine learning engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.

How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.

What cities in Michigan are hiring for Mlops Machine Learning Engineer jobs?

Cities in Michigan with the most Mlops Machine Learning Engineer job openings:

Infographic showing various Mlops Machine Learning Engineer job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 22% Part Time, 2% Temporary, 2% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AI Machine Learning Engineer (AI / ML: Python / Go)

Benzinga

Detroit, MI • On-site, Remote

Full-time

Re-posted 12 hours ago


Job description

About Benzinga

Benzinga is a fast-growing financial media and data technology company reshaping how investors access information. We combine artificial intelligence, machine learning, and real-time data pipelines to surface insights before they hit the mainstream. Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading banks, fintechs, and AI companies worldwide.

We're seeking a highly motivated AI / Machine Learning Engineer who thrives at the intersection of data science and backend engineering - someone who can take a model from notebook to production, and architect intelligent systems in Go and Python that scale to millions of requests.

The ideal candidate is a self-starter who independently identifies opportunities, experiments with new approaches, and ships production-ready solutions without constant direction.

________________________________________

Key Responsibilities

AI / Machine Learning

  • Research, design, and deploy machine learning models across NLP, time-series forecasting, and event detection domains.
  • Build LLM-driven systems (e.g. summarization, RAG pipelines, embedding search) optimized for financial news and quantitative data.
  • Develop model serving APIs and scalable inference layers using Go or Python.
  • Implement model monitoring, drift detection, and continuous retraining pipelines.
  • Work with financial text (earnings call transcripts, filings, news) to extract structured insights.
  • Collaborate with data engineers to build training datasets, feature stores, and embedding databases.

Backend & Infrastructure

  • Develop and maintain high-performance Python or Go microservices that integrate with AI systems and Go data APIs.
  • Design and optimize real-time inference pipelines on AWS, leveraging ECS/EKS, S3, and Lambda.
  • Ensure low-latency, fault-tolerant, and scalable delivery of AI-powered data.
  • Implement CI/CD for ML workflows, including containerization, automated deployment, and versioning.
  • Partner with DevOps to manage cloud infrastructure and ensure robust observability for AI workloads.

Requirement for applying:

  • During your screening you will be required to submit a Loom video walkthrough of your most exceptional product, share relevant code/repo links, and describe the biggest challenge you faced building it.

Required Qualification

  • 4+ years of experience in AI/ML or data engineering roles, with a proven track record of deploying ML models in production.
  • Computer science degree (Bachelor minimum)
  • Deep proficiency in Python (data, ML) and Go (backend, microservices).
  • Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face.
  • Experience with transformer architectures, embeddings, or fine-tuning LLMs.
  • Strong understanding of data pipelines, feature extraction, and model lifecycle management.
  • Familiarity with Docker, Kubernetes, and AWS (EKS, S3, Lambda, EC2).
  • Excellent problem-solving skills and ability to work independently in a distributed environment.

Preferred Skills / Experience

  • Startup experience.
  • Financial services or fintech background
  • Experience building LLM-powered APIs or retrieval-augmented generation (RAG) systems.
  • Knowledge of vector databases (e.g., Pinecone, Weaviate, FAISS, OpenSearch kNN).
  • Experience with Kafka, LangChain, or data streaming architectures.
  • Familiarity with financial data systems, real-time analytics, or news NLP.
  • Exposure to MLOps tools (MLflow, BentoML, SageMaker, Airflow, etc.).
  • Contributions to open-source ML or Go projects are a strong plus.

Tech Stack

  • Languages: Python, Go
  • ML Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain
  • Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
  • Containers & Orchestration: Docker, Kubernetes
  • Data & Streaming: Kafka, Postgres, OpenSearch
  • CI/CD: GitHub Actions, GitLab CI
  • Monitoring: Datadog, Prometheus, Grafana
  • Version Control: Git (Gitlab / Github)

________________________________________

Why Join Benzinga

  • Build and ship production AI systems that shape how financial markets understand information.
  • Operate with full creative freedom - explore, experiment, and execute your ideas end-to-end.
  • Work with a lean, highly technical team where initiative and ownership are celebrated.
  • Fully remote, high-trust environment that rewards curiosity, speed, and execution.
Employment Type: FULL_TIME

Benzinga logo

About Benzinga

Sourced by ZipRecruiter

Benzinga is a full-service news and media company with three main areas of expertise: real-time news, actionable trading ideas and insightful commentary. We offer coverage of all aspects of the financial market including corporate, economic and political content. With strong connections in and around the market, we strive to provide high quality and relevant news for the real-time environment that defines today's world.

Industry

Video and audio streaming services

Company size

11 - 50 Employees

Headquarters location

Detroit, MI, US

Year founded

2010

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