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Assistant Mlops Jobs in Texas (NOW HIRING)

AI / MLOps Support * Assist with deployment and monitoring of machine learning and AI applications. * Support AI workflows including model deployment and inference services. * Collaborate with Data ...

Google Cloud ML Engineer

Dallas, TX · On-site

$55.25 - $73.75/hr

Solid MLOps understanding (Docker, Kubernetes, CI/CD, Git). Experience with Agent Assist functionality is a plus. Preferred Qualifications: * Master's/PhD in Computer Science, AI/ML, or related field.

Machine Learning Developer

Dallas, TX · On-site

$115K - $140K/yr

This individual will create the MLOps framework, development standards, and platform foundation ... Establish operational visibility for ML systems and assist with troubleshooting and production ...

New

Machine Learning Developer

Dallas, TX · On-site

$115K - $140K/yr

This individual will create the MLOps framework, development standards, and platform foundation ... Establish operational visibility for ML systems and assist with troubleshooting and production ...

New

DevOps Engineer

Dallas, TX · On-site

$52.25 - $71.50/hr

AI / MLOps / AIOps * Deploy, manage, and optimize AI/ML workloads in production environments ... * Assist in performance tuning and resource optimization of AI/ML applications. * Evaluate ...

Senior DevOps Engineer

Dallas, TX · On-site

$128K - $165K/yr

AI / MLOps / AIOps * Deploy, manage, and optimize AI/ML workloads in production environments ... * Assist in performance tuning and resource optimization of AI/ML applications. * Evaluate ...

Enable enterprise use cases such as AI assistants, Microsoft Copilot-integrated workflows, task automation, and decision intelligence. * MLOps & LLMOps * Define and implement MLOps / LLMOps ...

Enable enterprise use cases such as AI assistants, Microsoft Copilot-integrated workflows, task automation, and decision intelligence. * MLOps & LLMOps * Define and implement MLOps / LLMOps ...

Enable enterprise use cases such as AI assistants, Microsoft Copilot-integrated workflows, task automation, and decision intelligence. * MLOps & LLMOps * Define and implement MLOps / LLMOps ...

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Assistant Mlops information

What is an Assistant MLOps?

Assistant MLOps are professionals who support the deployment, monitoring, and management of machine learning models in production environments. They assist senior MLOps engineers with tasks like automating workflows, managing data pipelines, maintaining infrastructure, and ensuring model performance. Their role bridges the gap between data science and IT operations, helping organizations scale and maintain their AI solutions efficiently. Assistant MLOps often have knowledge of cloud services, CI/CD tools, and basic programming, and they work closely with data scientists and engineers.

What are the typical daily responsibilities of an Assistant MLOps?

As an Assistant MLOps professional, you can expect your daily tasks to involve supporting the deployment, monitoring, and maintenance of machine learning models in production environments. This often includes collaborating with data scientists to automate model training and testing workflows, managing cloud-based resources, and ensuring that data pipelines are running smoothly. You'll also help troubleshoot issues related to model performance or infrastructure and assist in implementing best practices for version control and continuous integration. Working closely with both engineering and data teams, you'll play a key role in ensuring that ML models remain reliable and scalable in real-world applications.

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

To thrive as an Assistant MLOps, you need a solid understanding of machine learning fundamentals, programming (especially Python), and experience with cloud platforms; a degree in computer science or a related field is typically preferred. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and version control systems (e.g., Git) is important, and certifications in cloud services (AWS, Azure, GCP) can be advantageous. Strong problem-solving, communication, and collaboration skills help you bridge the gap between data science and operations teams. These combined skills ensure efficient deployment, monitoring, and maintenance of machine learning models in production environments.

What is the difference between Assistant Mlops vs Data Engineer?

AspectAssistant MlopsData Engineer
Required CredentialsCertifications in cloud platforms, basic scripting, ML toolsComputer science degree, SQL, Python, data architecture
Work EnvironmentCollaborates with ML teams, supports deployment pipelinesBuilds data pipelines, manages databases, processes large datasets
Industry UsageAI/ML projects, cloud-based environmentsData infrastructure, analytics, big data solutions

Assistant Mlops and Data Engineer roles share overlapping skills in cloud platforms and scripting. However, Assistant Mlops focuses on supporting ML deployment and operations, while Data Engineers primarily build and maintain data infrastructure. Both roles are essential in data-driven organizations but serve different functions within the data ecosystem.

What are the most commonly searched types of Mlops jobs in Texas?

The most popular types of Mlops jobs in Texas are:

What cities in Texas are hiring for Assistant Mlops jobs?

Cities in Texas with the most Assistant Mlops job openings:

Senior AI/ML Platform Engineer - GenAI & MLOps Lead

Plano, TX • On-site

$180 - $240/hr

Other

Medical, Retirement, PTO

Posted yesterday

New


Job description

Overview

Who we are Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world’s most admired brands, Toyota is growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We’re looking for talented team members who want to Dream. Do. Grow. with us. An important part of the Toyota family is Toyota Financial Services (TFS), the finance and insurance brand for Toyota and Lexus in North America. While TFS is a separate business entity, it is an essential part of this world-changing company- delivering on Toyota’s vision to move people beyond what’s possible. At TFS, you will help create best-in‑class customer experience in an innovative, collaborative environment.

Toyota does not offer support or sponsorship of job applicants for employment-based visas or any other work authorization for this role now or in the future. You must have the right to work in the United States and not require Toyota support or sponsorship for immigration-related employment (e.g., H-1B, O-1, E-3, H-1B1, TN, F-1 OPT, F-1 STEM OPT, F-1 CPT, TN, (job flexibility benefits) (also known as I-140 or Adjustment of State portability), etc.) now or in the future. You should not apply for this role if you will require Toyota to assist with immigration support or sponsorship now or in the future.

Who we’re looking for

Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Lead AI/ML Platform Engineer. The primary responsibility of this role is to design, build, and implement scalable platform solutions that power enterprise AI/ML and GenAI capabilities across the organization. You will help enable secure, production‑ready MLOps and LLMOps infrastructure that supports model training, inference, orchestration, and retrieval‑augmented generation. The Lead AI/ML Platform Engineer will support the Enterprise Platforms team’s objective to deliver reliable, secure, and high‑performing AI platform capabilities that drive business value at scale.

What you’ll be doing
  • Design and implement cloud‑native infrastructure that enables enterprise AI/ML and GenAI workloads in production
  • Build and evolve MLOps and LLMOps platform capabilities, including model training, versioning, deployment, monitoring, and rollback
  • Create GPU‑accelerated compute environments that improve model performance while balancing scalability and cost efficiency
  • Standardize infrastructure patterns for vector databases, model registries, and orchestration frameworks
  • Develop reusable approaches for model serving, inference scaling, prompt management, and latency optimization
  • Design secure, multi‑tenant environments with strong access controls, auditability, and usage governance for AI models
  • Partner closely with engineering, platform, and data teams to ensure smooth data flow, strong observability, and operational resiliency
  • Own technical direction for AI infrastructure services and integrations in collaboration with the architecture team
  • Lead design reviews, establish engineering standards, and help guide critical technical decisions
  • Mentor engineers, provide thoughtful feedback, and support growth through coaching and development planning
  • Stay current on emerging GenAI, distributed systems, and infrastructure trends to bring fresh ideas and better solutions to the team
What you bring
  • 10+ years of experience in software engineering, with a focus on cloud infrastructure or cloud platform engineering
  • 3+ years of experience building cloud infrastructure that supports AI/ML workloads such as training, tuning, and inference
  • Deep hands‑on experience with AWS and infrastructure‑as‑code tools such as Terraform, CDK, or CloudFormation
  • Experience with Kubernetes, containerization, and CI/CD pipelines in a production environment
  • Strong understanding of GPU infrastructure, serverless compute, and scalable microservice patterns
  • Familiarity with model hosting, inference scaling, and observability tools such as Datadog, CloudWatch, or Prometheus
  • Practical experience using Git/GitHub and CI/CD tooling such as GitHub Actions or Jenkins
  • Added bonus if you have Experience with AWS AI/ML services such as SageMaker or Bedrock
  • Familiarity with LLMOps tooling and GenAI infrastructure such as LangChain or RAG pipelines
  • Experience working with vector databases, model registries, or orchestration tools such as MLflow, Airflow, or Ray
  • Knowledge of prompt management, token usage optimization, and model performance tuning
  • AWS Solutions Architect Professional or Machine Learning certification
What we’ll bring
  • A collaborative work environment built on teamwork, flexibility, and respect
  • Professional growth programs including tuition reimbursement to advance your career
  • Team Member Vehicle Purchase Discount and Lease Vehicle Program (if applicable)
  • Comprehensive health care and wellness plans for you and your family
  • Toyota 401(k) Savings Plan with company match plus annual retirement contributions regardless of your participation
  • Paid holidays and paid time off for work‑life balance
  • Referral services for prenatal care, adoption, childcare, schooling, and more
  • Tax‑advantaged accounts including Health Savings Account (HSA), Health Care FSA, and Dependent Care FSA
Belonging at Toyota

Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Toyota is proud to have 10+ different Business Partnering Groups across 100 different North American chapter locations that support team members’ efforts to dream, do and grow without questioning that they belong.

Applicants for our positions are considered without regard to race, ethnicity, national origin, sex, sexual orientation, gender identity or expression, age, disability, religion, military or veteran status, or any other characteristics protected by law.

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