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Mlops Jobs in Rochester, NY (NOW HIRING)

MegazoneCloud Global

Data Engineer

Rochester, NY · On-site

$113K - $135K/yr

Preferred : • Experience with GenAI, AI/ML frameworks, and MLOps. • Certifications in Databricks, Snowflake, or AWS. Company : MegazoneCloud stands as Asia's leading cloud managed service ...

MegazoneCloud

Sr. Data Engineer

Rochester, NY · On-site

$145K - $165K/yr

Experience with GenAI , AI/ML frameworks, and MLOps. * Certifications in Databricks, Snowflake, or AWS. Why You'll Love It Here * Our Product is Our People: We live by this. We invest in people who ...

Megazone Cloud US

Sr. Data Engineer

Rochester, NY · On-site

$150 - $200/hr

Experience with GenAI , AI/ML frameworks, and MLOps. * Certifications in Databricks, Snowflake, or AWS. Why You\'ll Love It Here * Our Product is Our People: We live by this. We invest in people who ...

MegazoneCloud

Data Engineer

Rochester, NY · On-site

$110K - $140K/yr

Experience with GenAI , AI/ML frameworks, and MLOps. * Certifications in Databricks, Snowflake, or AWS. Why You'll Love It Here * Our Product is Our People: We live by this. We invest in people who ...

Tompkins Community Bank

Applied AI Engineer

Batavia, NY · On-site

$105K/yr

Establish and maintain MLOps practices to support model lifecycle management. * Oversee model versioning, experiment tracking, testing, deployment, monitoring, drift detection, and retraining ...

Mindex

AI Solutions Design Manager - Hybrid

Rochester, NY · On-site

$150 - $200/hr

Experience with MLOps, RAG, vector databases, and prompt engineering. * Previous leadership experience across hybrid technical/business teams. Success Indicators * Delivery of AI/GenAI solutions with ...

MegazoneCloud

Sr. Solutions Architect AI

Rochester, NY · On-site

$170K - $195K/yr

Drive strategic relationships with AWS, Azure, GCP, and Databricks, ensuring our offerings leverage the latest innovations in GenAI and MLOps. * Set the Standard: Define best practices for ...

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Mlops information

What is 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 the key skills and qualifications needed to thrive as an MLOps engineer?

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.

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 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.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are the most commonly searched types of Mlops jobs in Rochester, NY?

The most popular types of Mlops jobs in Rochester, NY are:

What are popular job titles related to Mlops jobs in Rochester, NY?

For Mlops jobs in Rochester, NY, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in Rochester, NY look for?

The top searched job categories for Mlops jobs in Rochester, NY are:

What cities near Rochester, NY are hiring for Mlops jobs?

Cities near Rochester, NY with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Rochester, NY as of September 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 74% Physical, 5% Hybrid, and 21% Remote job distribution.

Data Scientist - Analytics as a Service

Fairport, NY • On-site

Ralliant
See more jobs at Ralliantexternal

$125 - $150/hr

Other

Posted 22 days ago

Job description

Data Scientist – Analytics as a ServicePosition Summary

The Senior Data Scientist is responsible for developing the advanced analytics, machine learning models and AI algorithms that power Qualitrol's Analytics as a Service portfolio. Working closely with the Product Owner, Product Engineers and Data Engineer, this individual transforms industrial data into scalable analytics services that deliver measurable customer value.

Analytics & Model Development
  • Rotating machine condition monitoring
  • Grid monitoring
  • Predictive maintenance
  • Fault detection
  • Anomaly detection
  • Asset health assessment
  • Failure prediction
  • Fleet benchmarking

Design algorithms that are accurate, explainable and production-ready.

Data Mining & Feature Engineering
  • Sensor data
  • Time-series data
  • Event logs
  • Operational history
  • Maintenance records
  • Customer operating conditions

Develop robust feature engineering pipelines to improve model accuracy and scalability.

AI & Machine Learning
  • Machine learning models
  • Statistical models
  • Generative AI applications
  • Large Language Model integrations
  • Predictive analytics
  • Recommendation engines

Leverage AI-assisted tools to accelerate experimentation, model development and validation.

Production Deployment
  • Deploy models into production
  • Monitor model performance
  • Improve inference accuracy
  • Reduce computational costs
  • Continuously retrain models

Ensure analytics are scalable, reliable and maintainable.

Customer Value Creation

Partner with Product Owner and Customer Success to understand customer use cases and translate them into differentiated analytics capabilities.

Use customer feedback and operational data to continuously improve algorithms and business outcomes.

Required Experience
  • Master's or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics or related field
  • 5+ years developing machine learning or industrial analytics solutions
  • Strong Python programming experience
  • Experience with cloud-based ML environments
  • Experience deploying production AI models
  • Strong statistical and analytical skills
Preferred Experience
  • Industrial AI
  • Utilities
  • Rotating machinery
  • Power systems
  • Time-series analytics
  • Azure Machine Learning
  • AWS SageMaker
  • MLOps
  • LLMs and Generative AI
Success Measures
  • Within 12 months:
  • Multiple production analytics models deployed
  • Measurable improvement in prediction accuracy
  • Repeatable MLOps pipeline established
  • Analytics capabilities contributing to customer adoption
  • Continuous model improvement process operational

#LI-PW1

Ralliant, originally part of Fortive, now stands as a bold, independent public company driving innovation at the forefront of precision technology. With a global footprint and a legacy of excellence, we empower engineers to bring next‑generation breakthroughs to life — faster, smarter, and more reliably. Our high‑performance instruments, sensors, and subsystems fuel mission‑critical advancements across industries, enabling real‑world impact where it matters most. At Ralliant we're building the future, together with those driven to push boundaries, solve complex problems, and leave a lasting mark on the world.

QUALITROL manufactures monitoring and protection devices for high value electrical assets and OEM manufacturing companies. Established in 1945, QUALITROL produces thousands of different types of products on demand and customized to meet our individual customers’ needs. We are the largest and most trusted global leader for partial discharge monitoring, asset protection equipment and information products across power generation, transmission, and distribution. At Qualitrol, we are redefining condition‑based monitoring.

We Are an Equal Opportunity Employer. Ralliant Corporation and all Ralliant Companies are proud to be equal opportunity employers. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity or expression, or other characteristics protected by law. Ralliant and all Ralliant Companies are also committed to providing reasonable accommodations for applicants with disabilities. Individuals who need a reasonable accommodation because of a disability for any part of the employment application process, please contact us at applyassistance@Ralliant.com.

Pay RangeThe salary range for this position (in local currency) is 104300.00-193700.00

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