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Mlops Manager 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 ... management. · Build and support containerized ML workloads and deployment workflows using ...

KPMG

Manager, AI Engineer

Denver, CO

Manage AI projects including architecture design, model development, integration, and testing, and ensure projects follow best practices in MLOps, security, compliance, and scalability to support ...

KPMG

Manager, AI Engineer

Denver, CO

Manage AI projects including architecture design, model development, integration, and testing, and ensure projects follow best practices in MLOps, security, compliance, and scalability to support ...

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Mlops Manager information

What is an MLOps manager?

MLOps Managers are professionals responsible for overseeing the deployment, operation, and scaling of machine learning models in production environments. They coordinate teams to ensure seamless collaboration between data scientists, engineers, and IT staff, facilitating the automation of machine learning workflows. Their role involves managing infrastructure, optimizing processes for model monitoring and maintenance, and ensuring compliance with organizational and industry standards. MLOps Managers play a key role in bridging the gap between model development and operationalization, ensuring that machine learning solutions are reliable, reproducible, and scalable.

What are the key skills and qualifications needed to thrive as an MLOps manager?

To thrive as an MLOps Manager, you need expertise in machine learning, software engineering, and DevOps practices, often backed by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, Azure, GCP), and certifications such as AWS Certified Machine Learning or Google Cloud Professional ML Engineer are highly beneficial. Strong leadership, problem-solving, and cross-functional communication skills help manage teams and bridge the gap between data science and IT operations. These abilities are crucial for ensuring reliable, scalable, and efficient deployment of machine learning solutions in production environments.

What are some common challenges an MLOps manager faces when integrating machine learning models into production environments?

MLOps Managers often encounter challenges such as ensuring seamless collaboration between data science and engineering teams, managing model versioning, and maintaining reliable deployment pipelines. Balancing rapid experimentation with the need for robust, scalable, and secure production systems can be complex. Additionally, monitoring model performance post-deployment and handling data drift or model degradation are ongoing responsibilities. Effective communication and establishing standardized processes are key to overcoming these challenges and ensuring successful model operations.

What is the difference between Mlops Manager vs Data Scientist?

AspectMlops ManagerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; certifications in cloud platforms or MLOps toolsBachelor's/Master's in CS, Statistics, or related; certifications in data analysis or machine learning
Work EnvironmentCollaborates with engineering, DevOps, and data teams to deploy and maintain ML systemsAnalyzes data, builds models, and provides insights to inform business decisions
Employer & Industry UsageTech companies, AI startups, enterprises implementing ML pipelinesResearch institutions, tech firms, finance, healthcare, and marketing sectors

The Mlops Manager focuses on deploying, maintaining, and optimizing machine learning systems within an organization, working closely with engineering and DevOps teams. In contrast, a Data Scientist primarily analyzes data, develops models, and provides insights. While both roles require knowledge of machine learning, the Mlops Manager emphasizes operationalizing ML solutions, whereas the Data Scientist emphasizes data analysis and modeling.

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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For Mlops Manager jobs in Colorado, the most frequently searched job titles are:

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Mlops Manager jobs near you

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 3 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