Overview
Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.
Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.
We are looking for a skilled and proactive Data & Machine Learning DevOps Engineer to join our Data Science and Analytics team. This role is responsible for supporting and maintaining the production health of machine learning workflows, model deployments, and Snowflake data platform operations. You will serve as a key support engineer, ensuring seamless ML and data infrastructure performance across a wide range of tools and platforms.
Responsibilities
-
Provide
L2/L3 support
for machine learning operations and Snowflake platform workflows during India business hours. -
Monitor and troubleshoot issues related to
ML model deployment
, batch/real-time inference pipelines
, and data workflows
. -
Support
Snowflake DevOps operations
, including warehouse monitoring, job scheduling, query performance tuning, and access control. -
Ensure reliability of data ingestion and transformation pipelines built on
Matillion
, Fivetran
, and Snowpipe
. -
Support and enhance
CI/CD pipelines
for ML model packaging, testing, deployment, and rollback. -
Collaborate with Data Scientists, Engineers, and Cloud Platform teams to resolve incidents and improve system reliability.
-
Use
ServiceNow
and Jira
to manage and track support tickets, ensure SLA compliance, and provide timely resolution or escalation. -
Proactively build monitoring tools, runbooks, and automation scripts to improve incident response time and reduce manual effort.
-
Maintain logs, dashboards, and alerts for both ML and data infrastructure using appropriate monitoring tools (e.g., CloudWatch, Prometheus, Snowflake monitoring).
-
Ensure compliance with internal security, data privacy, and responsible AI standards.
Qualifications
-
Bachelor s degree in Computer Science, Engineering, or a related technical field.
-
3 6 years of experience in
ML Ops
, DevOps
, or Data Platform support
roles. -
Strong experience with
Snowflake
, including RBAC, warehouse tuning, streams/tasks, monitoring, and query troubleshooting. -
Hands-on support experience with
ML platforms
like DataRobot
, RapidCanvas
, SageMaker
, or custom-built frameworks. -
Experience with
CI/CD tools
(GitHub Actions, Azure DevOps, Jenkins) and scripting (Python, Bash). -
Exposure to data integration tools:
Matillion
, Fivetran
, and Snowpipe
. -
Understanding of
model monitoring
, version control, and retraining workflows. -
Experience with
incident response processes
, root cause analysis, and preventative remediation. -
Familiarity with
data governance
, compliance, and audit capabilities in Snowflake. -
Strong SQL skills and experience automating Snowflake tasks and managing workflows.
-
Excellent communication and documentation skills, with the ability to coordinate with global teams.
Careers Privacy Statement Keysight is an Equal Opportunity Employer.
-
Provide
L2/L3 support
for machine learning operations and Snowflake platform workflows during India business hours. -
Monitor and troubleshoot issues related to
ML model deployment
, batch/real-time inference pipelines
, and data workflows
. -
Support
Snowflake DevOps operations
, including warehouse monitoring, job scheduling, query performance tuning, and access control. -
Ensure reliability of data ingestion and transformation pipelines built on
Matillion
, Fivetran
, and Snowpipe
. -
Support and enhance
CI/CD pipelines
for ML model packaging, testing, deployment, and rollback. -
Collaborate with Data Scientists, Engineers, and Cloud Platform teams to resolve incidents and improve system reliability.
-
Use
ServiceNow
and Jira
to manage and track support tickets, ensure SLA compliance, and provide timely resolution or escalation. -
Proactively build monitoring tools, runbooks, and automation scripts to improve incident response time and reduce manual effort.
-
Maintain logs, dashboards, and alerts for both ML and data infrastructure using appropriate monitoring tools (e.g., CloudWatch, Prometheus, Snowflake monitoring).
-
Ensure compliance with internal security, data privacy, and responsible AI standards.
