... MLOps, Monitoring & Production Support Agentic AI Architecture Good to Have Role Highlights Build and deploy production fraud detection services Develop low-latency ML inference solutions Design ML ...
... MLOps, Monitoring & Production Support Agentic AI Architecture Good to Have Role Highlights Build and deploy production fraud detection services Develop low-latency ML inference solutions Design ML ...
AI/ML Engineer
Dallas, TX · On-site
MLOps * PySpark Education * Master''s degree in Computer Science, Information Technology ... entry level candidates are welcome , provided they have practical AI/ML projects and strong ...
AI/ML Engineer
Dallas, TX · On-site
MLOps * PySpark Education * Master''s degree in Computer Science, Information Technology ... entry level candidates are welcome , provided they have practical AI/ML projects and strong ...
AML and Sanctions- Data Scientist- Senior Associate
$77K - $202K/yr
Medical
Dental
Vision
Retirement
PTO
... MLOps practices for model monitoring - Comfort working with structured and unstructured data ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...
AML and Sanctions- Data Scientist- Senior Associate
$77K - $202K/yr
Medical
Dental
Vision
Retirement
PTO
... MLOps practices for model monitoring - Comfort working with structured and unstructured data ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...
Entry Level Mlops information
What is an entry level MLOps engineer?
An entry level MLOps (Machine Learning Operations) engineer is a professional who helps bridge the gap between data science and IT operations by managing, deploying, and monitoring machine learning models in production environments. They typically work under the supervision of more experienced engineers and focus on automating workflows, maintaining infrastructure, and ensuring models run smoothly at scale. Entry level MLOps engineers often use tools like Docker, Kubernetes, and cloud platforms, and collaborate with data scientists to streamline the model lifecycle from development to deployment.
What are the key skills and qualifications needed to thrive as an entry level MLOps engineer?
To thrive as an Entry Level MLOps Engineer, you need foundational knowledge in machine learning concepts, programming (typically Python), and cloud computing, often supported by a bachelor's degree in computer science or a related field. Experience with tools like Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS or Azure is commonly required. Strong problem-solving skills, attention to detail, and effective communication help you collaborate with data scientists and engineers. These skills ensure reliable model deployment, streamlined workflows, and successful integration of machine learning solutions into production environments.
What are typical challenges faced by entry level MLOps professionals, and how can they be addressed?
Entry-level MLOps professionals often face challenges such as bridging the gap between data science and IT operations, understanding deployment pipelines, and ensuring model reproducibility. It's common to work with unfamiliar tools and cloud platforms, which can be overwhelming at first. Gaining hands-on experience through projects, seeking mentorship from senior team members, and actively participating in knowledge-sharing sessions can help overcome these hurdles and accelerate your learning. Additionally, clear communication with both data scientists and engineers is key to successful collaboration in this role.
What is the difference between Entry Level Mlops vs Data Engineer?
| Aspect | Entry Level Mlops | Data Engineer |
|---|---|---|
| Required Credentials | Bachelor's in CS, Data Science, or related field; familiarity with cloud platforms | Bachelor's in CS, Software Engineering, or related; knowledge of databases and ETL processes |
| Work Environment | Collaborates with data scientists and DevOps teams on deploying ML models | Builds and maintains data pipelines and infrastructure for analytics |
| Industry Usage | Used in tech, finance, healthcare for deploying ML solutions | Common in tech, e-commerce, finance for data management |
Entry Level Mlops focuses on deploying and maintaining machine learning models, often working closely with data scientists. Data Engineers build and manage data pipelines and infrastructure. While both roles require knowledge of cloud platforms and programming, Mlops emphasizes model deployment and monitoring, whereas Data Engineers focus on data architecture and processing.
What are the most commonly searched types of Mlops jobs in Texas?
The most popular types of Mlops jobs in Texas are:
What are popular job titles related to Entry Level Mlops jobs in Texas?
For Entry Level Mlops jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Entry Level Mlops jobs in Texas look for?
The top searched job categories for Entry Level Mlops jobs in Texas are:
What cities in Texas are hiring for Entry Level Mlops jobs?
Cities in Texas with the most Entry Level Mlops job openings:
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This job post has expired 1 day ago. Applications are no longer accepted.
Job description
Hiring: Machine Learning Engineer Fraud Detection
Location: Dallas, TX 100% Onsite
Contract: 1 Year
Experience: 8 12 Years
We are looking for a strong Machine Learning Engineer with experience building and supporting production-grade fraud detection solutions.
Key SkillsPython & Machine Learning
Real-Time / Low-Latency Inference
REST APIs & Microservices
Google Cloud Platform & Databricks
Neo4j / Graph Databases
Feature Stores & Feature Engineering
Data Pipelines & Data Engineering
MLOps, Monitoring & Production Support
Agentic AI Architecture Good to Have
Build and deploy production fraud detection services
Develop low-latency ML inference solutions
Design ML feature engineering pipelines
Integrate ML models with APIs and microservices
Support graph-based fraud detection using Neo4j
Improve performance, scalability and reliability
Work with MLOps teams on releases and production support
Interested candidates can share their updated resumes at:
#Hiring #MachineLearning #MachineLearningEngineer #MLEngineer #FraudDetection #Python #Google Cloud Platform #Databricks #Neo4j #MLOps #Microservices #DallasJobs #TexasJobs #ContractJobs #TechJobs
About Compugra Systems
Sourced by ZipRecruiter
Company size
11 - 50 Employees
Headquarters location
South Plainfield, NJ, US
Year founded
2002