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Mlops Jobs in Rochester, NY (NOW HIRING)

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 : AWS, DataBricks, Snowflake and GCP Partner of the Year! AI-Native ...

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 ...

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 ...

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 ...

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

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 ...

... MLOps tooling and CI/CD pipelines for ML - Experience with vector databases and semantic search architectures - Translating complex business problems into AI solution designs - Contributing to ...

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.

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.

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 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 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:
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What cities near Rochester, NY are hiring for Mlops jobs? Cities near Rochester, NY with the most Mlops job openings:
Infographic showing various Mlops job openings in Rochester, NY as of August 2026, with employment types broken down into 91% Full Time, 1% Part Time, and 8% Contract. Highlights an 73% Physical, 6% Hybrid, and 21% Remote job distribution.

Principal Data Scientist (for Greece) (Town of Greece)

Fut-ure Recruitment and Technology

North Greece, NY • On-site

Full-time

Medical

Posted 14 days ago


Job description

About the client

Our client is a global technology company with a strong focus on AI innovation, large‑scale data applications, and modern product delivery. Operating in multiple international markets, the company encourages autonomy, experimentation, and engineering leadership in all areas of data science and machine learning. Teams are cross‑functional, highly collaborative, and empowered to bring real business impact through AI‑driven solutions.

Role Highlights:
  • Position: Principal Data Scientist
  • Type of work: Hybrid from Athens.
  • Work from office requirements: 2 days per week from the office, 3 days per week from the office. 100% remote option is NOT possible.
  • Type of contract: Full‑time employment contract.
  • Contract location: Greece
  • Type of company: International growing tech company with operations in 15+ markets with around 2,500 employees across several continents.
  • Years of experience required: 9+ years of experience.
  • Must‑have tech skills: experience leveraging large‑scale data to build ML/DL models with Python, MLOps, experience in GenAI application development (nice to have: big data technologies).
  • Industry: Information technology, entertainment
  • Relocation support: Relocation support for Greek expats.
  • Bonuses, perks, benefits: competitive pay and bonus scheme, career development, monthly meal allowance, private health insurance for the employee and their family members, continuous training and unlimited access to Udemy.
  • Reasons why it is a great place to work? International company, growing team, exciting project, the company is among the Best Workplaces in Europe and certified Great Place to Work across their offices.
  • Work permit requirements: Only candidates with valid work permit for European Union (EU) will be evaluated for this role.
About the role

As a Principal Data Scientist, you will be responsible for leading the design, development, and deployment of advanced machine learning and deep learning systems. You’ll guide the technical roadmap for AI initiatives, mentor other scientists, and drive innovation with scalable, production‑ready solutions. This role is ideal for a senior AI professional with both strategic vision and deep technical expertise across multiple ML disciplines.

Tech stack

Projects are implemented using Python and its broader ML ecosystem, with production‑level MLOps practices. The infrastructure may include big data tools such as Apache Spark, Delta Lake, Kafka, and Flink. Experience with foundational models, prompt engineering, or GenAI platforms is also a plus.

What will you do
  • Design scalable architectures and cutting‑edge algorithms for AI‑powered systems.
  • Lead the technical direction of the AI team, aligning with product and business objectives.
  • Own the entire lifecycle of AI projects, from ideation and experimentation to production and monitoring.
  • Deliver high‑impact proofs of concept that demonstrate measurable business value.
  • Mentor senior and junior data scientists across technical and professional development areas.
  • Guide strategic prioritization of AI initiatives, balancing innovation with value delivery.
  • Explore and apply the latest advancements in AI research to stay at the forefront of the field.
  • Communicate technical strategies effectively across both technical and non‑technical stakeholders.
Ideally you bring
  • 8+ years of experience designing, implementing, and deploying ML/DL models in production environments using Python.
  • PhD‑level expertise (or equivalent experience) in one or more of the following: recommender systems, NLP, computer vision, speech/audio processing, time‑series modeling, reinforcement learning, graph‑based learning.
  • Strong understanding of the ML lifecycle, including data pipelines, training, evaluation, deployment, and monitoring.
  • Solid experience with MLOps tools and practices for maintaining robust AI systems at scale.
  • Proven ability to drive architecture decisions, model optimization, and scalability improvements.
  • Strong communication and stakeholder management skills across technical and business teams.
  • (Nice to have) Practical experience with GenAI systems or agent‑based AI workflows, including prompt engineering and model evaluation techniques.
  • (Nice to have) Familiarity with distributed data technologies such as Spark, Delta Lake, Kafka, or NoSQL systems.
Compensation & Benefits

Attractive salary plus performance‑based incentives, opportunities for professional growth, monthly meal package, private health coverage, and continuous education and skill‑building resources.

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