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Assistant Mlops Jobs (NOW HIRING)

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 ...

TMS

AI Architect- Insurance domain

Edison, NJ · Remote

$75/hr

You will define end-to-end AI architecture (data → model → MLOps → serving), ensure secure ... assist, and personalization. * Select and guide model approaches: predictive ML , LLMs/GenAI , NLP ...

Juvare

$125K - $160K/yr

Experience supporting AI/MLOps workflows is a plus. Location * Atlanta / Remote Must Have * Cloud ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

Juvare

Senior DevOps Engineer

Atlanta, GA · On-site +1

$125K - $160K/yr

Experience supporting AI/MLOps workflows is a plus. Location * Atlanta / Remote Must Have * Cloud ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

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Assistant Mlops information

What is a $900,000 AI job?

A $900,000 AI-related job typically refers to high-level roles such as senior AI engineers, machine learning directors, or AI research leads, often in large tech companies or specialized firms. These positions usually require advanced skills in machine learning, deep learning, and data science, along with extensive experience and sometimes executive responsibilities.

Is MLOps a good career path?

MLOps is a growing field that combines machine learning, software engineering, and operations to deploy and maintain machine learning models at scale. It offers high demand for skills in cloud platforms, automation, and data management, making it a promising career choice for those interested in AI and infrastructure. Professionals in MLOps often work with tools like Docker, Kubernetes, and CI/CD pipelines, and typically require knowledge of programming and cloud services.

What are 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 jobs pay $2000 a day?

High-paying jobs that can reach $2000 a day often include specialized roles such as senior software engineers, data scientists, or MLOps engineers with extensive experience and advanced skills. These positions typically require advanced certifications, expertise in machine learning tools, cloud platforms, and often involve consulting or contract work with flexible schedules.

Which 3 jobs will survive AI?

Assistant MLOps roles are likely to persist because they involve managing complex machine learning systems, requiring skills in cloud platforms, automation, and data engineering. Jobs that involve creative thinking, strategic decision-making, and tasks requiring emotional intelligence, such as data scientists, AI ethics specialists, and machine learning engineers, are also expected to remain in demand despite AI advancements.

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 some typical daily responsibilities for an Assistant MLOps professional?

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, and why are they important?

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.
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Assistant Mlops jobs near you
Infographic showing various Assistant Mlops job openings in the United States as of June 2026, with employment types broken down into 4% As Needed, 4% Full Time, 80% Part Time, and 12% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution.
Lead AI Engineer -- Semiconductor AI Innovation

Lead AI Engineer -- Semiconductor AI Innovation

Onto Innovation

Wilmington, MA • On-site

$112K - $147K/yr

Full-time

Posted 7 days ago

Job description

Job Summary:
Onto Innovation is a leader in semiconductor technologies, and they are seeking a Lead AI Engineer to drive AI-powered solutions for semiconductor equipment operations. This hands-on leadership role involves defining AI strategies, mentoring a team, and integrating AI solutions into production environments.
Responsibilities:
• Define the AI strategy and architecture for integrating machine learning into core engineering and manufacturing processes.
• Partner with tool, process, and applications engineers to map as-is processes and define a to-be AI/automation architecture and deliver measurable outcomes.
• Ship agentic assistants for use-cases. Stand up LLM + RAG + tool integrations (via MCP servers) that help engineers accelerate tool operation/setup/maintenance and explain trade-offs, grounded in internal docs, logs, and historical inspection outcomes.
• Lead projects across diverse areas: Predictive maintenance for tool health monitoring and failure detection. Computer vision for wafer defect detection, segmentation, and classification. LLM-based engineering assistants using RAG and MCP agents to make internal knowledge more accessible. Process optimization & yield improvement through data-driven insights and parameter tuning. Simulation and digital twins to model process behaviors and accelerate R&D.
• Build retrieval-augmented AI assistants to query internal knowledge bases, tools, and logs.
• Architect robust pipelines for data ingestion, labeling, storage, and retrieval across massive multi-modal datasets (images, telemetry, recipes, logs).
• Stand up scalable MLOps infrastructure: model registries, monitoring, evaluation, deployment, and governance.
• Hire, mentor, and manage a team of 3 engineers focused on LLM/Agents, CV/ML, and MLOps/Data.
• Work cross-functionally to integrate AI solutions into production environments safely and securely.
Qualifications:
Required:
• 5+ years applied ML/AI experience, with 3+ years in a technical leadership role.
• Hands-on expertise with at least two of the following domains: Large Language Models - RAG, fine-tuning, agent frameworks, prompt optimization; Predictive Modeling - tool failure prediction, anomaly detection, time-series analysis; Computer Vision - defect detection, segmentation, or SEM/optical imaging.
• Strong background in ML systems architecture and production deployment.
• Advanced Python proficiency: C++/CUDA familiarity is a plus.
• Experience with MLOps stacks: containers, CI/CD, Ray Serve/Triton, model registries (e.g., MLflow), and GPU optimization.
• Strong stakeholder collaboration skills and the ability to translate between engineering, operations, and leadership.
• Demonstrated success delivering AI-powered products into production.
Preferred:
• Familiarity with semiconductor manufacturing, inspection, or metrology.
• Understanding of fab interfaces and data connectivity (SECS/GEM, GEM300).
• Prior experience deploying digital twins or simulation-driven optimization.
• Knowledge of vector databases, retrieval pipelines, and hybrid search.
• Experience implementing safety, security, and IP protections for AI systems.
• Exposure to datasets or tools from KLA, ASML, Applied Materials, Onto, Nova, or similar inspection/metrology vendors.
Company:
Onto Innovation stands alone in process control with our unique perspective across the semiconductor value chain. Founded in 2019, the company is headquartered in Wilmington, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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