Company
Technology
SoftwareEngineerII-MLOps
Neural analysis suggests this role is
optimal for Mid candidates.
“Software Engineer II - MLOps. Skills: MLOps, Machine learning models, CI/CD pipelines, Cloud infrastructure. Design robust infrastructure for ML models. Build robust infrastructure for ML models”
What You'll Achieve.
Ensure operational readiness; Improve system architecture for scalability; Improve system architecture for uptime; Improve system architecture for cost efficiency; Enhance the MLOps ecosystem; Support knowledge sharing; Support consistency across teams
Industry & Context.
Root cause analysis; Troubleshooting
What They're Looking For.
Must Have
3+ years of experience in MLOps, Python and backend engineering principles, Deploying, monitoring, and maintaining ML models, Workflow orchestration tools such as Apache Airflow, Distributed data processing or streaming technologies, Building CI/CD pipelines, Cloud-based infrastructure and modern DevOps practices, Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience, Communication and collaboration skills, Proactive, detail-oriented mindset, Focus on automation and system reliability, Leverage AI tools to improve productivity
Nice to Have
Kafka or Spark experience
What You'll Do.
Design robust infrastructure for ML models
Build robust infrastructure for ML models
Maintain robust infrastructure for ML models
Develop end-to-end ML pipelines
Optimize end-to-end ML pipelines
Ensure operational readiness
Build CI/CD pipelines
Implement monitoring systems
Implement logging systems
Implement alerting systems
Improve system architecture
Document engineering standards
Document operational procedures
How You'll Work.
Team & Collaboration
Cross-functional engineering environments; Data scientists; Product engineers
Communication Scope
Knowledge sharing
Process & Methodology
Agile
Full Job Description
## Accountabilities Design, build, and maintain robust infrastructure for deploying, monitoring, and managing machine learning models in production environments. Develop and optimize end-to-end ML pipelines, including feature engineering, model training workflows, deployment, and continuous evaluation. Collaborate closely with data scientists and product engineers to productionize models and ensure operational readiness. Build and maintain CI/CD pipelines to support automated, reliable, and reproducible machine learning deployments. Implement monitoring, logging, and alerting systems to ensure model performance, system reliability, and early detection of issues. Improve system architecture for scalability, uptime, and cost efficiency across distributed environments. Evaluate and integrate new tools, frameworks, and best practices to enhance the MLOps ecosystem. Document engineering standards, workflows, and operational procedures to support knowledge sharing and consistency across teams. Requirements: 3+ years of experience in MLOps, Data Engineering, or infrastructure-focused software engineering roles. Strong proficiency in Python and backend engineering principles. Hands-on experience deploying, monitoring, and maintaining machine learning models in distributed production systems. Solid understanding of workflow orchestration tools such as Apache Airflow. Experience with distributed data processing or streaming technologies such as Kafka or Spark. Proven experience building CI/CD pipelines and automated software delivery workflows. Familiarity with cloud-based infrastructure and modern DevOps practices. Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience. Strong communication and collaboration skills in cross-functional engineering environments. Proactive, detail-oriented mindset with a strong focus on automation and system reliability. Demonstrated ability to leverage AI tools to improve productivity and engineerin
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