Job Description:
">
We are seeking a highly skilled Machine Learning Operations Engineer to join our team. As a Machine Learning Operations Engineer, you will be responsible for designing, building, and maintaining scalable, reliable, and efficient machine learning platforms.
You will work closely with infrastructure and DevOps teams to integrate machine learning models into the broader platform for continuous operation and scaling. Your key responsibilities will include:
1. Leading the design, build, and maintenance of machine learning platforms to ensure robust performance and operational excellence.
2. Collaborating with infrastructure and DevOps teams to integrate machine learning models into the broader platform.
3. Improving architecture, scalability, stability, and performance of the machine learning platform, focusing on AWS cloud engineering solutions and platform services.
4. Developing processes for model monitoring and governance to ensure successful ML model operationalization and compliance with industry standards.
5. Extending existing machine learning libraries and frameworks to enhance performance and integration within the platform.
6. Defining objectives for the machine learning platform, owning the technical roadmap, and ensuring alignment with platform engineering goals.
7. Establishing and upholding standards for platform engineering and operational excellence, striving for best-in-class ML platforms.
8. Designing and implementing architectural best practices for efficient and scalable deployment of ML solutions.
Required Skills and Qualifications:
To be successful in this role, you will need:
1. Hands-on experience in Cloud Engineering, especially with AWS services such as EC2, S3, Lambda, and SageMaker.
2. Proficiency in platform engineering practices and frameworks for MLOps.
3. Development skills in Python or relevant languages.
4. Experience with DevOps tools such as Git, Jenkins, GitHub Actions, or similar.
5. Experience with data pipeline tools like Apache Airflow, AWS Glue, or similar.
6. Experience with containerization and orchestration tools like Docker and Kubernetes.
7. Familiarity with Groovy is an advantage.
8. Knowledge of Azure OpenAI, Langgraph, Bedrock is a plus.
9. Good communication skills and ability to work collaboratively.
Benefits:
This is a permanent role with a hybrid work model. The package includes competitive salary, bonus, pension, healthcare, life insurance, laptop, phone, extensive training resources, company discounts, on-site parking, and more.
Additional Preferences:
Desirable qualifications include:
* AWS Certified Solutions Architect or AWS Certified Machine Learning specialty.
* Exposure to CI/CD tools and practices.
* Experience with big data technologies such as Apache Spark or Hadoop.
Requirements:
Please note that you must be eligible to work in Ireland/EU.