MLOps Engineer, Data Solutions & Initiatives
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Monthly Salary:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
We are looking for a world-class MLOps Engineer who is passionate about operational excellence through automation and machine learning engineering processes. As an MLOps Engineer you will play a crucial role in ensuring the seamless integration of machine learning development and production operations to deliver best in class and highly available ML will work with data science data engineering and AI/ML engineering teams to understand model deployment and infrastructure requirements while promoting efficiency scalability security and reliability throughout the machine learning lifecycle. Your expertise in cloud platforms ML automation model monitoring and ML infrastructure management will be crucial to the success of our AI/ML responsibilities will include:- Designing ML engineering platforms and tooling that simplify the process of building training deploying and operating machine learning models at scale.- Developing automation for model training pipelines deployment workflows model monitoring and ML operational tasks.- Monitoring ML model performance data drift model accuracy and system availability and remediating as necessary.- Collaborating with data science and ML engineering teams on finding operationally sustainable solutions to model deployment and lifecycle management challenges.- Provide guidance to the engineering organization on navigating ML governance model security and compliance requirements for AI/ML systems.- Mentoring other MLOps engineers around ML best practices model deployment strategies and MLOps design approaches.
- 3 years in MLOps Engineering or ML Platform Engineering roles
- 2 years in Data Engineering Software Engineering or Data Science roles
- Proficiency with containerization and orchestration technologies such as Docker Kubernetes or ML-specific platforms like Kubeflow.
- Proficiency with programming and scripting languages such as Python R SQL or Bash with strong Python expertise for ML workflows.
- Experience with infrastructure-as-code and ML pipeline tools (e.g. Terraform MLflow Airflow Prefect).
- Proficiency in CI/CD for ML pipelines using Jenkins or ML-specific platforms like Kubeflow Pipelines.
- Proficiency with ML monitoring and observability technologies such as MLflow Prometheus Grafana or model drift detection tools.
- Proficiency with designing and operating ML workloads in a public cloud environment (AWS SageMaker GCP Vertex AI etc.).
- Experience integrating ML governance model security and compliance practices into all stages of the ML lifecycle.
- Continuously work with data science and ML engineering teams to improve model reliability implementing actionable ML monitoring frameworks and participate in ML system on-call.
- Analytical & problem solving skills with understanding of ML concepts ability to communicate technical ML concepts clearly.
- Excellent communication and collaboration abilities with experience bridging data science and engineering teams.
- Bachelors Degree in Computer Science Data Science Machine Learning or equivalent in Engineering
- 4 years in MLOps Engineering ML Platform Engineering or equivalent ML operational experience
- 3 years in Software Engineering Data Engineering or ML Engineering roles with focus on production ML systems
- Expertise in operating and integrating with cloud ML services such as AWS SageMaker EKS CloudWatch S3 or equivalent GCP ML services
- Experience with large-scale distributed ML systems and data platforms such as Spark Snowflake Ray or Kubernetes-based ML frameworks
- Experience with ML model governance AI compliance frameworks and preparing for ML-specific audits (model validation bias testing explainability requirements)
Key Skills
- Apache Hive
- S3
- Hadoop
- Redshift
- Spark
- AWS
- Apache Pig
- NoSQL
- Big Data
- Data Warehouse
- Kafka
- Scala
About Company
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