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Mlops Jobs in Springfield, OH (NOW HIRING)

Altamira Technologies Corp.

AI/ML Engineer

Fairborn, OH · On-site

Experience with MLOps * Familiarity with Docker * Experience with performance tuning * Experience working in hybrid or air-gapped environment * Python * C/C++ * Rust Altamira is an Equal Opportunity ...

Worky

Distinguished AI Software Engineer

Dayton, OH · On-site

$190 - $230/hr

Collaborate with AI/ML, data, and simulation teams to enable model integration and inference services within production software (familiarity with MLOps concepts preferred). * Evaluate new tools and ...

Phase2 Technology

AI Developer

Beavercreek, OH · On-site

$77.50 - $176/hr

Ability to implement MLOps or DevSecOps practices such as CI/CD, containerization, automated testing, or model evaluation * Secret clearance * Bachelor's degree in Computer Science, Software ...

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All JobsMlops JobsMlops Jobs in Springfield, OH

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 cities near Springfield, OH are hiring for Mlops jobs?

Cities near Springfield, OH with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Springfield, OH as of June 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution.

R&D Technical Lead

Altamira Technologies Corp.

Fairborn, OH • On-site

$150 - $180/hr

Other

Posted 12 days ago

Job description

Position Description

We are seeking an experienced R&D Technical Lead to bridge the gap between AI/ML research and production-ready solutions. In this role, you will provide technical leadership across AI/ML and software engineering, guiding teams from early-stage research concepts through successful customer delivery.

The ideal candidate brings a broad software engineering background combined with strong AI/ML expertise, with the ability to translate machine learning models and research into scalable, integrated software architectures. You will collaborate across disciplines to drive technical decisions, mentor development teams, and ensure solutions meet real-world customer needs.

Our work focuses on applied AI/ML solutions for real-world object tracking, detection, and characterization, enabling advanced data triaging and system automation. These capabilities help our customers manage increasing volumes of data and address the challenges associated with data saturation.

Core Responsibilities
  • Team Leadership: Provide technical guidance to engineering teams, aligning project requirements to technical tasking
  • System Architecture:Design and implement scalable, high-performance software systems that integrate AI/ML capabilities
  • Collection Coordination:Architect multi-agent or agentic workflows to synchronize data collection efforts across various projects, optimizing resource allocation.
  • Technical Mentorship:Lead a high-performing team of engineers, conducting code reviews and setting engineering standards for MLOps and production pipelines.
  • Strategic Integration:Collaborate with project managers to translate complex client requirements into actionable AI/ML roadmaps.
Qualifications & Skills
  • Education:Bachelor’s or above in Computer Science, AI, or a related quantitative field.
  • Experience: 7+ years of professional experience in machine learning, with at least 3 years leading teams from research or prototyping through production deployment.
  • Computer Vision Expertise:Proven track record in developing computer vision models forobject tracking(e.g., CNNs, Transformers) and real-time video analytics.
  • Advanced Programming:Expert-level proficiency inPython and high-performance languages (C++/Rust/Java) alongside familiarity with backend deployment tools (Docker, Kubernetes).
  • Data Management:Experience overseeing the full data lifecycle, from acquisition and cleaning to high-fidelity labeling for specialized collections.
  • Communication:Ability to articulate complex AI concepts to non-technical stakeholders and executive leadership.
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