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Mlops Jobs in Colorado (NOW HIRING)

eTeam

Generative AI Developer

Denver, CO · On-site

The role involves strong Python development and experience with various AI and machine learning frameworks, focusing on deploying models and managing MLOps systems. Responsibilities : • Strong ...

Paradigm

Senior ML Ops Engineer

Denver, CO · On-site

$123K - $170K/yr

What You Will Do: · Design, build, and maintain scalable MLOps solutions that support the end-to-end machine learning lifecycle, including model training, deployment, monitoring, and retraining. · ...

Crusoe

Senior Staff Solutions Engineer (NYC)

Denver, CO · On-site

$56.75 - $73.25/hr

Kubernetes + MLOps Focus: Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow) Design infrastructure that balances performance, scalability, and efficiency.

Crusoe

Senior Staff Solutions Engineer (NYC)

Denver, CO · On-site

$56.75 - $73.25/hr

Kubernetes + MLOps Focus: Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow) Design infrastructure that balances performance, scalability, and efficiency.

ClifyX

AWS Senior Architect

Denver, CO · On-site

$66.75 - $87.50/hr

Implement MLOps pipelines using embeddings, fault detection models, LangGraph, and vLLM. • Advanced AI Development: Hands-on experience with LangChain, LangFuse, Llama 3.2 LLM, and RAG-based ...

DaVita, Inc.

AI Technical Sr Lead (IT)

Denver, CO · On-site +1

Deployment, Automation & MLOps: * Implement end-to-end CI/CD pipelines for automated model training, deployment, and seamless rollback capabilities. * Standardize LLMOps and MLOps observability ...

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All JobsMlops JobsMlops Jobs in Colorado

Mlops information

See Colorado salary details

$95.5K

$149.9K

$178.4K

How much do mlops jobs pay per year?

As of Aug 11, 2026, the average yearly pay for mlops in Colorado is $149,942.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,671.00 and $162,851.00 per year, depending on experience, location, and employer.

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 Colorado? The most popular types of Mlops jobs in Colorado are:
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What cities in Colorado are hiring for Mlops jobs? Cities in Colorado with the most Mlops job openings:
Mlops jobs near you
Infographic showing various Mlops job openings in Colorado as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $149,942 per year, or $72.1 per hour.

Senior MLOps Platform Engineer {S}

Careers - Stratagem - Make a Lasting Impact

Colorado Springs, CO • On-site, Remote

$90K - $123K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 24 days ago

Job description

ARKA Group L.P. (“ARKA”) is an advanced technologies company serving the U.S. military, intelligence community, and commercial space industry delivering next-generation solutions to support the national security space enterprise. Built on more than six decades of excellence, ARKA brings modern approaches and a culture of innovation to the challenges of today.

Join the ARKA team to learn how Beyond Begins Here. Discover your next career opportunity now!

Position Overview:

Our AI Center of Excellence builds the next generation of Agentic AI products that autonomously reason, plan, and act on behalf of our customers. To deliver these capabilities at scale, we need a platform engineering group that provides a robust, secure, and highly available MLOps foundation across both on premise clusters and AWS. The team works closely with data scientists, product engineers, and SREs to turn experimental models into reliable services that power mission critical applications. 

In support of work/life balance, many positions are available for a flexible schedule within the pay period.  Ask us about the opportunity for flex scheduling if that’s of interest to you. 

Why join us 

  • Shape the end-to-end lifecycle of cutting-edge AI services—from model training to production inference. 
  • Influence architecture decisions for a hybrid cloud environment that will serve thousands of concurrent agents. 
  • Collaborate with world-class researchers and product teams while enjoying a strong engineering culture focused on automation, observability, and reliability. 

      Responsibilities: 

      • Design, implement, and operate a unified MLOps platform that supports both on-premise Kubernetes clusters and AWS. The platform should enable rapid onboarding of new Agentic AI services and provide consistent governance across environments. 
      • Develop reusable CI/CD pipelines (GitLab CI) for model packaging, containerization, automated testing, canary releases, and rollbacks. 
      • Build observability, monitoring, and alerting stacks (Prometheus, Grafana, OpenTelemetry, CloudWatch) to track inference latency, throughput, resource utilization, and data drift for real time and batch workloads. 
      • Create self-service tooling (CLI, SDKs, UI dashboards) that allows data science and product teams to register models, define inference endpoints, and manage versioning without deep DevOps involvement. 
      • Architect and maintain data pipelines that feed training data, model artifacts, and inference logs into a governed data lake (S3, on prem object store). 
      • Collaborate with research and product engineers to translate experimental Agentic AI prototypes into production grade services, ensuring reproducibility, security, and compliance. 
      • Drive performance optimization for inference workloads (GPU/CPU scaling, model quantization, batching strategies) 
      • Champion best practices in security (IAM, network policies, secret management), cost efficiency, and disaster recovery for the hybrid infrastructure. 
      • Mentor junior engineers and contribute to internal knowledge bases, upskilling, and review processes. 

      Required Qualifications:

      • BS in computer science or related engineering field
      • 5+years of experience building and operating production grade software infrastructure, preferably in a hybrid onprem / cloud environment
      • Deep expertise with Kubernetes (cluster provisioning, Helm, operators, custom resources) and container runtimes (Docker, OCI)
      • Hands on experience with AWS services (EKS, SageMaker, S3, IAM, CloudWatch, Step Functions) and the ability to bridge onprem resources with AWS via VPN/Direct Connect
      • Strong software engineering skills in Python and at least one compiled language (Go, Rust, or Java) for building platform components and SDKs
      • Proficiency with CI/CD and GitOps tooling (Argo CD, Flux, Gitlab, GitHub Actions, or similar)
      • Solid understanding of distributed systems (consensus, fault tolerance, load balancing) and experience tuning high throughput, low latency inference pipelines
      • Experience with data engineering frameworks (Airflow, Prefect, Kafka, Spark, Flink) and building robust, versioned data pipelines
      • Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry, ELK) and the ability to define meaningful SLIs/SLOs for AI services
      • Track record of collaborating with research or product teams to move prototypes to production, translating experimental code into maintainable services
      • Strong problem solving mindset, excellent written and verbal communication, and a passion for building scalable AI platforms

      Preferred Qualifications:

      • Working knowledge of Scrum and Agile software development methodology

      Location: Remote

      This is a remote position that will primarily be supporting our Aurora, CO and King of Prussia, PA locations.  Due to contract requirements, the job has to be performed from a remote location in the United States.

      What We Offer:

      • Comprehensive medical/vision/dental insurance packages
      • Company contributions to qualified HSA accounts
      • 401k retirement plan with industry leading company contributions
      • 3 weeks of vacation accrual per year plus time off for sick leave and unscheduled life events
      • 13 paid holidays
      • Upfront tuition assistance for approved degree programs
      • Annual bonus program based on company and employee performance
      • Company paid life insurance, AD&D, Short-Term and Long-Term disability insurance
      • 4 weeks paid Parental Leave
      • Employee assistance program (EAP)

      EHS/Environmental Requirements:

      This job operates alongside a professional office environment. While performing the duties of this job, the employee routinely is required to use hands to keyboard, communicate, listen to, and interpret instructions and remain stationary for extended periods of the time. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the job.

      Applicants are invited to apply for a reasonable accommodation to perform the essential duties of the job. To apply, send a request to staffing@arka.org or contact 203-797-5000 and press 2 for Human Resources.

      ITC & Security Clearance Requirements:

      This position requires the incumbent to access export-controlled information. If you are not a U.S. Person, any offer is contingent upon the Company's ability to obtain a special license granting you access. This could take several months. You will not be able to begin employment until such license is obtained.

      Visa Restrictions:

      No visa sponsorship is available for this position.

      Pre-employment Screenings:

      Employment with any ARKA companies in the U.S. is contingent upon satisfactory completion of several pre-employment requirements to include a credit check, background check, and drug screen.

      Apply