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Lead Systems Engineer - Data DevOps/MLOps
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign, deploy and manage CI/CD pipelines for data integration and ML model deployment
Build cloud infrastructure for processing, training and serving ML models
Automate data validation, transformation and orchestration workflows
Ensure data versioning, lineage tracking and reproducibility across ML workflows
Improve model serving, performance monitoring and reliability in production
From the employer’s posting
Design, deploy, and manage CI/CD pipelines for seamless data integration and ML model deployment
Establish robust infrastructure for processing, training, and serving machine learning models using cloud-based solutions
Automate critical workflows such as data validation, transformation, and orchestration for streamlined operations
Ensure data versioning, lineage tracking, and reproducibility across ML experiments and workflows
Improve model serving, performance monitoring, and reliability in production ecosystems
Tools in this posting
- Python
- AWS
- Azure
- Databricks
- dbt
- Docker
- GCP
- Grafana
- Hadoop
- Hive
- Kubernetes
- MLflow
- Spark
- Terraform
- Airflow
- pandas
- PyTorch
- TensorFlow
Source — Tool mentions in context
We are seeking a skilled and passionate Lead Systems Engineer with Data DevOps/MLOps expertise to drive innovation and efficiency across our data and machine learning operations. Responsibilities Design, deploy, and manage CI/CD pipelines for seamless data integration and ML model deployment Establish robust infrastructure for processing, training, and serving machine learning models using cloud-based solutions Automate critical workflows such as data validation, transformation, and orchestration for streamlined operations Collaborate with cross-functional teams, including data scientists and engineers, to integrate ML solutions into production environments Improve model serving, performance monitoring, and reliability in production ecosystems Ensure data versioning, lineage tracking, and reproducibility across ML experiments and workflows Identify and implement opportunities to improve scalability, efficiency, and resilience of the infrastructure Enforce rigorous security measures to safeguard data and ensure compliance with relevant regulations Debug and resolve technical issues in data pipelines and ML deployment workflows Requirements Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field 8+ years of experience in Data DevOps, MLOps, or related disciplines Expertise in cloud platforms such as Azure, AWS, or GCP Skills in Infrastructure as Code tools like Terraform, CloudFormation, or Ansible Proficiency in containerization and orchestration technologies such as Docker and Kubernetes Hands-on experience with data processing frameworks including Apache Spark and Databricks Proficiency in Python with familiarity with libraries including Pandas, TensorFlow, and PyTorch Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions Experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow Understanding of monitoring and alerting tools like Prometheus and Grafana Strong problem-solving and independent decision-making capabilities Effective communication and technical documentation skills Nice to have Background in DataOps methodologies and tools such as Airflow or dbt Knowledge of data governance platforms like Collibra Familiarity with Big Data technologies such as Hadoop or Hive Showcase of certifications in cloud platforms or data engineering tools
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Oct 7, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordYour next step
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Has EPAM Systems posted this job before?
Yes. We’ve followed one other Lead Systems Engineer - Data DevOps/MLOps job there since November 2026. It is still up.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
We are seeking a skilled and passionate Lead Systems Engineer with Data DevOps/MLOps expertise to drive innovation and efficiency across our data and machine learning operations. Responsibilities Design, deploy, and manage CI/CD pipelines for seamless data integration and ML model deployment Establish robust infrastructure for processing, training, and serving machine learning models using cloud-based solutions Automate critical workflows such as data validation, transformation, and orchestration for streamlined operations Collaborate with cross-functional teams, including data scientists and engineers, to integrate ML solutions into production environments Improve model serving, performance monitoring, and reliability in production ecosystems Ensure data versioning, lineage tracking, and reproducibility across ML experiments and workflows Identify and implement opportunities to improve scalability, efficiency, and resilience of the infrastructure Enforce rigorous security measures to safeguard data and ensure compliance with relevant regulations Debug and resolve technical issues in data pipelines and ML deployment workflows Requirements Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field 8+ years of experience in Data DevOps, MLOps, or related disciplines Expertise in cloud platforms such as Azure, AWS, or GCP Skills in Infrastructure as Code tools like Terraform, CloudFormation, or Ansible Proficiency in containerization and orchestration technologies such as Docker and Kubernetes Hands-on experience with data processing frameworks including Apache Spark and Databricks Proficiency in Python with familiarity with libraries including Pandas, TensorFlow, and PyTorch Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions Experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow Understanding of monitoring and alerting tools like Prometheus and Grafana Strong problem-solving and independent decision-making capabilities Effective communication and technical documentation skills Nice to have Background in DataOps methodologies and tools such as Airflow or dbt Knowledge of data governance platforms like Collibra Familiarity with Big Data technologies such as Hadoop or Hive Showcase of certifications in cloud platforms or data engineering tools
Source details
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- Pay
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- Location & working pattern
India
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