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Cloud-Optimized Machine Learning Platform Specialist

2 weeks ago

Letterkenny, Donegal, Ireland beBeeMachineLearning Full time
Job Title

Machine Learning Operations Engineer

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The Role
  1. Design, Build and Maintain Machine Learning Platforms: Lead the design, build, and maintenance of scalable, reliable, and efficient machine learning platforms, ensuring robust performance and operational excellence.
  2. Integrate Machine Learning Models: Collaborate with infrastructure and DevOps teams to integrate machine learning models into the broader platform for continuous operation and scaling.
  3. Improve Architecture and Scalability: Improve architecture, scalability, stability, and performance of the machine learning platform, focusing on AWS cloud engineering solutions and platform services.
  4. Model Monitoring and Governance: Develop processes for model monitoring and governance, ensuring successful ML model operationalization and compliance with industry standards.
  5. Extend Machine Learning Libraries: Extend existing machine learning libraries and frameworks to enhance performance and integration within the platform.
  6. Define Technical Roadmap: Define objectives for the machine learning platform, own the technical roadmap, and ensure alignment with platform engineering goals.
  7. Establish Operational Excellence: Establish and uphold standards for platform engineering and operational excellence, striving for best-in-class ML platforms.
  8. Deploy ML Solutions: Design and implement architectural best practices for efficient and scalable deployment of ML solutions.


Required Skills and Qualifications
  1. Cloud Engineering Experience: Hands-on experience in Cloud Engineering, especially with AWS services such as EC2, S3, Lambda, and SageMaker.
  2. Platform Engineering Practices: Proficiency in platform engineering practices and frameworks for MLOps.
  3. Development Skills: Development skills in Python or relevant languages.
  4. DevOps Tools Experience: Experience with DevOps tools such as Git, Jenkins, GitHub Actions, or similar.
  5. Data Pipeline Tools Experience: Experience with data pipeline tools like Apache Airflow, AWS Glue, or similar.
  6. Containerization and Orchestration Tools Experience: Experience with containerization and orchestration tools like Docker and Kubernetes.
  7. Familiarity with Groovy: Familiarity with Groovy is an advantage.
  8. Azure OpenAI, Langgraph, Bedrock Knowledge: Knowledge of Azure OpenAI, Langgraph, Bedrock is a plus.
  9. Good Communication and Collaboration Skills: 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
  1. AWS Certification: AWS Certified Solutions Architect or AWS Certified Machine Learning specialty is advantageous.
  2. CICD Tools and Practices Exposure: Exposure to CI/CD tools and practices.
  3. Big Data Technologies Experience: Experience with big data technologies such as Apache Spark or Hadoop.