They are seeking a motivated Machine Learning Operations Engineer to assist in the design and development of MLOps infrastructure, collaborating with data scientists and engineers to ensure efficient ...
They are seeking a motivated Machine Learning Operations Engineer to assist in the design and development of MLOps infrastructure, collaborating with data scientists and engineers to ensure efficient ...
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Quick apply
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Senior Full Stack Machine Learning Engineer information
See Springtown, TX salary details
$36.3K - $47.1K
1% of jobs
$47.1K - $57.9K
3% of jobs
$57.9K - $68.7K
4% of jobs
$68.7K - $79.6K
5% of jobs
$79.6K - $90.4K
11% of jobs
$91K is the 25th percentile. Wages below this are outliers.
$90.4K - $101.2K
15% of jobs
The median wage is $108.3K / yr.
$101.2K - $112K
17% of jobs
$112K - $122.8K
17% of jobs
$124.9K is the 75th percentile. Wages above this are outliers.
$122.8K - $133.7K
13% of jobs
$133.7K - $144.5K
9% of jobs
$144.5K - $155.3K
5% of jobs
$36.3K
$109.9K
$155.3K
How much do senior full stack machine learning engineer jobs pay per year?
What is the difference between Senior Full Stack Machine Learning Engineer vs Data Scientist?
| Aspect | Senior Full Stack Machine Learning Engineer | Data Scientist |
|---|---|---|
| Credentials | Bachelor's/Master's in CS, Data Science, or related; experience with ML frameworks | Bachelor's/Master's in Statistics, Data Science, or related; strong analytical skills |
| Work Environment | Develops end-to-end ML applications, integrates backend and frontend | Analyzes data, builds models, visualizes insights |
| Industry Usage | Tech, finance, healthcare, where deploying ML models is essential | Research, analytics, consulting across various sectors |
While both roles involve working with data and machine learning, the Senior Full Stack Machine Learning Engineer focuses on building and deploying complete ML solutions, including frontend and backend integration. In contrast, Data Scientists primarily analyze data and develop models to generate insights. The engineer's role is more application-oriented, whereas the Data Scientist's role is more research and analysis-focused.
Full-time
Posted 17 days ago
Job description
CLA is a top 10 national professional services firm dedicated to creating opportunities for clients, people, and communities. They are seeking a motivated Machine Learning Operations Engineer to assist in the design and development of MLOps infrastructure, collaborating with data scientists and engineers to ensure efficient deployment and monitoring of machine learning models.
Responsibilities:
• Assist with design and development of MLOps infrastructure to support the deployment, management, and monitoring of machine learning models within the firm.
• Collaborate with data scientists and engineers to ensure seamless integration of data science and engineering processes.
• Support maintenance of automated workflows and pipelines for efficient model deployment and monitoring.
• Troubleshoot and resolve any issues with model performance, data pipelines, or infrastructure.
• Assist with ensuring the security and scalability of the MLOps infrastructure to handle large volumes of data and models.
• Support maintenance of documentation for MLOps processes and systems.
• Collaborate with cross-functional teams to understand business needs and translate them into technical requirements.
• Ensure compliance with data security and privacy regulations.
• Stay up to date with industry trends and advancements in machine learning and MLOps.
Qualifications:
Required:
• 2+ years' experience as a Data/ML Engineer
• Bachelor's degree is required. Combination of relevant experience, education, and training may be accepted in lieu of degree.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP)
• Basic understanding of SQL and experience with ETL pipeline design and maintenance
• Knowledge of CI/CD concepts and experience with tools like Jenkins, Git, Perforce, etc.
• Proficiency in programming languages such as Python
• Knowledge of data pipeline architecture and data transformation
• Knowledge of machine learning model deployment and management
• Familiarity with performance monitoring and optimization techniques
• Basic understanding of machine learning and DevOps principles
Company:
CLA exists to create opportunities for our clients, our people, and our communities through industry-focused wealth advisory, outsourcing, audit, tax, and consulting services. Founded in 1998, the company is headquartered in Alpharetta, USA, with a team of 5001-10000 employees. The company is currently Late Stage.