Head of AI
OR · On-site +1
This executive role owns practice P&L, builds scalable offerings (GenAI, ML, data science, AI platforms, MLOps), and partners with sales and delivery leaders to expand revenue and customer impact ...
Head of AI
OR · On-site +1
This executive role owns practice P&L, builds scalable offerings (GenAI, ML, data science, AI platforms, MLOps), and partners with sales and delivery leaders to expand revenue and customer impact ...
Head of AI
OR · Remote
This executive role owns practice P&L, builds scalable offerings (GenAI, ML, data science, AI platforms, MLOps), and partners with sales and delivery leaders to expand revenue and customer impact ...
Head of AI
OR · Remote
This executive role owns practice P&L, builds scalable offerings (GenAI, ML, data science, AI platforms, MLOps), and partners with sales and delivery leaders to expand revenue and customer impact ...
Senior Lead System Architect
OR · Remote
$132K - $176K/yr
Experience building scalable AI platforms with modern data, MLOps, observability, and infrastructure automation practices. * Exceptional communication, leadership, and stakeholder management skills ...
Senior Lead System Architect
OR · Remote
$132K - $176K/yr
Experience building scalable AI platforms with modern data, MLOps, observability, and infrastructure automation practices. * Exceptional communication, leadership, and stakeholder management skills ...
Mlops information
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 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 the difference between Mlops vs Data Engineer?
| Aspect | Mlops | Data Engineer |
|---|---|---|
| Primary Focus | Deploying, managing, and monitoring machine learning models in production | Building and maintaining data pipelines and infrastructure for data processing |
| Skills & Certifications | Machine learning, DevOps, cloud platforms, scripting | SQL, ETL, data warehousing, programming |
| Work Environment | Collaborates with data scientists, software engineers, and DevOps teams | Works with data analysts, data scientists, and software developers |
| Industry Usage | AI/ML projects, production environments, cloud services | Data 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.
Is MLOps outdated?
MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.
What is the average salary in MLOps?
The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.
What are popular job titles related to Mlops jobs in Remote, OR?
For Mlops jobs in Remote, OR, the most frequently searched job titles are:
What cities near Remote, OR are hiring for Mlops jobs?
Cities near Remote, OR with the most Mlops job openings:
Full-time
Re-posted 28 days ago
Key responsibilities
Define and execute the North America AI practice strategy and capability roadmap.
Support the development of packaged offerings, industry-specific solutions, and represent the company externally through thought leadership activities.
Own pipeline and bookings targets, ensure high-quality delivery, and build and scale the AI team.
Job description
About Atos Group
Atos Group is a global leader in digital transformation with c. 67,000 employees and annual revenue of c. €10 billion, operating in 61 countries under two brands - Atos for services and Eviden for products. European number one in cybersecurity, cloud and high performance computing, Atos Group is committed to a secure and decarbonized future and provides tailored AI-powered, end-to-end solutions for all industries. Atos Group is the brand under which Atos SE (Societas Europaea) operates. Atos SE is listed on Euronext Paris.
The purpose of Atos Group is to help design the future of the information space. Its expertise and services support the development of knowledge, education and research in a multicultural approach and contribute to the development of scientific and technological excellence. Across the world, the Group enables its customers and employees, and members of societies at large to live, work and develop sustainably, in a safe and secure information space.
Position : Head of Artificial Intelligence
Role Summary
The Head of Artificial Intelligence Practice (North America) leads strategy, growth, delivery excellence, and talent for the AI portfolio across U.S. and Canada. This executive role owns practice P&L, builds scalable offerings (GenAI, ML, data science, AI platforms, MLOps), and partners with sales and delivery leaders to expand revenue and customer impact across industries.
Reporting and Scope
Key Responsibilities
• Practice strategy and roadmap
• Portfolio, offerings, and thought leadership
• Go-to-market and revenue growth
• Delivery excellence and customer outcomes
• Talent, org design, and culture
• Governance, security, and responsible AI
• Financial management and operations
Required Qualifications
Preferred Qualifications
Core Competencies
Tools and Technologies (Representative)
Work Environment
Equal Opportunity
The organization is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Here at Atos, diversity and inclusion are embedded in our DNA. Read more about our commitment to a fair work environment for all.
Atos is a recognized leader in its industry across Environment, Social and Governance (ESG) criteria. Find out more on our CSR commitment.
Choose your future. Choose Atos.
Atos Group is a global leader in digital transformation with c. 67,000 employees and annual revenue of c. €10 billion, operating in 61 countries under two brands - Atos for services and Eviden for products. European number one in cybersecurity, cloud and high performance computing, Atos Group is committed to a secure and decarbonized future and provides tailored AI-powered, end-to-end solutions for all industries. Atos Group is the brand under which Atos SE (Societas Europaea) operates. Atos SE is listed on Euronext Paris.
The purpose of Atos Group is to help design the future of the information space. Its expertise and services support the development of knowledge, education and research in a multicultural approach and contribute to the development of scientific and technological excellence. Across the world, the Group enables its customers and employees, and members of societies at large to live, work and develop sustainably, in a safe and secure information space.
Position : Head of Artificial Intelligence
Role Summary
The Head of Artificial Intelligence Practice (North America) leads strategy, growth, delivery excellence, and talent for the AI portfolio across U.S. and Canada. This executive role owns practice P&L, builds scalable offerings (GenAI, ML, data science, AI platforms, MLOps), and partners with sales and delivery leaders to expand revenue and customer impact across industries.
Reporting and Scope
- Reports to: Head of Data & AI - North America
- Direct reports: AI practice leadership team (solution leads, delivery leaders, architects), plus dotted-line matrix teams
- Geography: United States and Canada (remote/hybrid depending on location)
- Travel: Up to 25-40% (client sites, executive briefings, industry events)
Key Responsibilities
• Practice strategy and roadmap
-
- Define the North America AI practice strategy aligned to corporate goals and market demand.
- Build and maintain a 12-24 month capability roadmap across GenAI, Agentic AI, applied ML, AI engineering, MLOps, and AI governance.
- Identify strategic bets (industries, partnerships, platforms) and prioritize investments for impact and scale.
- Lead the delivery team for AI across NA including building and managing talent and ensuring ulitization targets for the team
• Portfolio, offerings, and thought leadership
-
- Support Head of Advisory and Global Teams with packaged offerings and accelerators (POCs-to-production playbooks, reference architectures, reusable components).
- Work with Head of Innovation industry-specific solutions (e.g., banking, retail, healthcare, telecom, public sector) with measurable outcomes.
- Represent the company externally through speaking, publishing, analyst briefings, and customer success stories.
• Go-to-market and revenue growth
-
- Own pipeline and bookings targets for AI services in North America; partner with sales, alliances, and marketing.
- Help Represent the solutioning for strategic pursuits, including executive-level proposal narratives, value cases, and pricing models.
- Help support the partner ecosystem with hyperscalers and AI platform vendors; drive co-sell motions where applicable.
• Delivery excellence and customer outcomes
-
- Ensure high-quality delivery across AI engagements, with strong governance, risk management, and stakeholder communication.
- Standardize delivery methodology for AI programs (discovery, prototyping, productionization, monitoring, continuous improvement).
- Drive customer adoption, measurable business value, and referenceability through disciplined success management.
• Talent, org design, and culture
-
- Build and scale a high-performing AI team: hiring plans, skills frameworks, career paths, and mentorship.
- Upskill broader delivery teams through training programs, communities of practice, and internal enablement.
- Foster a culture of engineering rigor, responsible AI, collaboration, and continuous learning.
• Governance, security, and responsible AI
-
- Establish responsible AI standards (privacy, security, bias mitigation, explainability, model risk management).
- Partner with security, legal, and compliance teams to ensure AI solutions meet client and regulatory requirements.
- Define and monitor AI practice quality standards, including data governance, model lifecycle controls, and audits.
• Financial management and operations
-
- Help support the Head of Data and AI P&L: revenue, gross margin, utilization, bench, subcontractor mix, and investment planning.
- Set operating rhythm: QBRs, forecast accuracy, capacity planning, and delivery health dashboards.
- Optimize delivery model (onshore/nearshore/offshore) to meet client needs and margin targets.
Required Qualifications
- 15+ years of experience in technology consulting, product engineering, or enterprise technology leadership, with 8+ years in AI/ML/GenAI leadership.
- Proven track record building and scaling an AI practice or portfolio with P&L responsibility and measurable revenue growth.
- Strong understanding of modern AI stack: GenAI (LLMs), Agentic AI, ML, data engineering, AI platforms, MLOps/LLMOps, and cloud services (AWS/Azure/GCP).
- Demonstrated experience delivering AI solutions end-to-end: discovery to production, monitoring, and continuous improvement.
- Executive presence with ability to influence EVP-level stakeholders and lead complex deal pursuits.
- Experience leading multi-disciplinary teams (AI Architects, AI Engineers, data scientists, ML engineers, architects, product managers, delivery leaders).
- Knowledge of responsible AI, security, and privacy requirements for enterprise AI implementations.
Preferred Qualifications
- Experience in IT services/consulting organizations operating with global delivery models (onshore/nearshore/offshore).
- Domain expertise in one or more regulated industries (financial services, healthcare, telecom, government).
- Partnership experience with hyperscalers and AI platform vendors; co-sell and alliance management success.
- Hands-on experience with AI solution architecture, including retrieval-augmented generation (RAG), vector databases, and agentic workflows.
- Advanced degree in Computer Science, Engineering, Data Science, or related field; MBA a plus.
Core Competencies
- Strategic leadership and business building
- Consultative selling and executive stakeholder management
- AI engineering rigor and delivery governance
- Productization mindset (offerings, accelerators, repeatability)
- Talent development and organizational design
- Cross-functional collaboration in matrix environments
- Strong communication: narrative building, proposals, and presentations
Tools and Technologies (Representative)
- Cloud: AWS, Microsoft Azure, Google Cloud Platform
- AI/ML: Agentic AI, Python, PyTorch/TensorFlow, scikit-learn, MLflow (or equivalent), feature stores
- GenAI: LLM APIs and platforms, prompt engineering, RAG patterns, evaluation frameworks, guardrails
- Data: SQL, modern data warehouses/lakehouse platforms, streaming where needed
- MLOps/LLMOps: CI/CD for models, monitoring/observability, model registry, governance tooling
Work Environment
- Remote or hybrid within North America; may require proximity to major client hubs.
- Travel expectation up to 25-40% based on client needs and business development cycles.
- This role may require occasional work outside standard business hours to support executive meetings across time zones.
Equal Opportunity
The organization is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Here at Atos, diversity and inclusion are embedded in our DNA. Read more about our commitment to a fair work environment for all.
Atos is a recognized leader in its industry across Environment, Social and Governance (ESG) criteria. Find out more on our CSR commitment.
Choose your future. Choose Atos.