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Assistant Mlops Jobs (NOW HIRING)

ClifyX

Prinicipal AI Engineer

West Des Moines, IA · On-site

... adoption of GenAI, LLMs, MLOps frameworks, and AI platforms. • Architect scalable, high ... assistants. • Develop reusable AI frameworks, components, and internal toolkits to accelerate ...

CreditOne

AVP, Artificial Intelligence

Las Vegas, NV · On-site

Description Position Summary The Assistant Vice President of Artificial Intelligence (AVP of AI) is ... Implement AIOps/MLOps and model governance practices aligned with banking regulations and internal ...

MSR Cosmos

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.

CNA

Senior Data AI Engineer

Chicago, IL · On-site

$118K - $141K/yr

At CNA, we strive to create a culture in which people know they matter and are part of something ... Productionize and operationalize AI solutions and advanced analytics in a DevOps/MLOps environment ...

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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 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 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.

Is assistant MLOps in high demand?

Assistant MLOps roles are increasingly in demand as organizations expand their machine learning and AI initiatives. These positions often require knowledge of cloud platforms, automation tools, and deployment pipelines, reflecting the growing need for scalable and reliable ML systems across industries.
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Assistant Mlops jobs near you
Infographic showing various Assistant Mlops job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution.

Data Engineer - Agentic AI & ML Ops (Co-op)

Campbell's

Camden, NJ • On-site

$115K - $138K/yr

Full-time

Re-posted 26 days ago

Job description

Since 1869, we've connected people through food they love. We're proud to be stewards of amazing brands that people trust. Our portfolio includes the iconic Campbell's brand, as well as Cape Cod, Chunky, Goldfish, Kettle Brand, Lance, Late July, Pacific Foods, Pepperidge Farm, Prego, Pace, Rao's Homemade, Snack Factory, Snyder's of Hanover.Swanson, and V8.

Here, you will make a difference every day. You will be supported to build a rewarding career with opportunities to grow, innovate and inspire. Make history with us.

Operational Support Data Engineer - Agentic AI & ML Ops (Co-op)

  • We are seeking a motivated and curious Data Engineer - Agentic AI & ML Ops to join our Enterprise Data & Analytics team. This co-op provides hands-on experience supporting cloud-based data platforms, AI/ML operations, Generative AI, and Agentic AI solutions.
  • You will work with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, Python, PySpark, SQL, LLMs, and modern AI/ML frameworks in an Agile environment.
  • If you are passionate about data engineering, AI, and automation, we want to hear from you.

Key Responsibilities

  • Build Data & AI Pipelines: Develop and support ETL/ELT pipelines and AI/ML workflows.
  • Data Integration & Transformation: Ingest, transform, and orchestrate data using Python, PySpark, and SQL.
  • Develop Agentic AI Solutions: Build and test AI agents and intelligent workflows for automation and data access.
  • LLM & Prompt Engineering: Design and optimize prompts and workflows using LLMs and GenAI frameworks.
  • AI/ML Development & Automation: Build Python-based scripts, APIs, and notebooks on cloud platforms.
  • Support Analytics & AI/ML: Prepare datasets for reporting, ML models, forecasting, and advanced analytics.
  • Monitor & Support Operations: Troubleshoot pipeline failures, performance issues, and data quality gaps.
  • MLOps & CI/CD: Support deployment, testing, and automation for data and AI solutions.
  • Data Modeling & Semantic Layers: Assist with STTM, data modeling, and reporting datasets.
  • Agile Collaboration: Participate in sprint planning, stand-ups, and retrospectives.
  • Documentation & Automation: Create runbooks, workflows, and technical documentation.

Learning & Development Opportunities

  • Hands-on experience with Databricks, Snowflake, Azure, ADLS, ADF, and Power BI.
  • Exposure to MLOps, GenAI, Agentic AI, LLMs, CI/CD, and automation.
  • Experience working with AI agents, prompt engineering, and workflow orchestration.
  • Mentorship from Data, AI/ML, and Platform Engineers.
  • Experience in Agile/Scrum and DevOps/MLOps environments.

Qualifications

  • Pursuing a degree in Computer Science, Data Engineering, Data Science, AI/ML, or related field.
  • Knowledge of SQL, Python/PySpark, ETL/ELT, and APIs.
  • Familiarity with Databricks, Snowflake, Azure, ADLS, ADF, Power BI, or Git is a plus.
  • Exposure to LLMs, GenAI, Agentic AI, MLOps, or CI/CD is beneficial.
  • Experience with Python-based AI/ML projects or notebooks is a plus.
  • Strong analytical and problem-solving skills.
  • Strong communication and teamwork skills.

The Company is committed to providing equal opportunity for employees and qualified applicants in all aspects of the employment relationship, including consideration for employment, without regard to race, color, sex, sexual orientation, gender identity, national origin, citizenship, marital status, protected veteran status, disability, age, religion, or any other classification protected by law.

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