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Senior MLOPs Engineer

Posted 5 months agoViewed

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💎 Seniority level: Senior, 8 years with a Bachelor's degree or 6 years with a Master's degree

🔍 Industry: Multicloud solutions

🏢 Company: Rackspace👥 1001-5000💰 Private over 7 years ago🫂 Last layoff almost 2 years agoIaaSBig DataCloud ComputingCloud Infrastructure

🗣️ Languages: English

⏳ Experience: 8 years with a Bachelor's degree or 6 years with a Master's degree

🪄 Skills: LeadershipPythonApache HadoopGCPHadoopJavaKerasMachine LearningC++AlgorithmsData StructuresSparkTensorflowCommunication SkillsC (Programming language)

Requirements:
  • Proven track record in designing and implementing cost-effective and scalable ML inference systems.
  • Hands-on experience with leading deep learning frameworks such as TensorFlow, Keras, or Spark MLlib.
  • Solid foundation in machine learning algorithms, natural language processing, and statistical modeling.
  • Strong grasp of fundamental computer science concepts like algorithms, distributed systems, data structures, and database management.
  • Experience in Apache Hadoop ecosystem (Oozie, Pig, Hive, Map Reduce).
  • Expertise in public cloud services, particularly in GCP and Vertex AI.
  • Proficient in applying model optimization techniques (distillation, quantization, hardware acceleration).
  • Recent experience in Java.
  • In-depth understanding of LLM architectures, parameter scaling, and deployment trade-offs.
  • Technical degree: Bachelor's degree in Computer Science or Master's degree with relevant industry experience.
  • Specialization in Machine Learning is preferred.
Responsibilities:
  • Architect and optimize existing data infrastructure for machine learning and deep learning models.
  • Collaborate with cross-functional teams to translate business objectives into engineering solutions.
  • Own end-to-end development and operation of high-performance, cost-effective inference systems.
  • Provide technical leadership and mentorship to the engineering team.
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  • Minimum 3 years of experience in deploying and managing ML models.
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  • Skilled in creating feature engineering processes and inference pipelines.
  • Strong programming skills in Python.
  • Experience with distributed computing frameworks like Spark (PySpark).
  • Experience with ML platforms like Airflow or other orchestration frameworks (SageMaker, Kubeflow, MLFlow).
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  • Experience with DevOps concepts, CI/CD pipelines, and data security measures.
  • Hands-on experience in data engineering within Big Data ecosystems.
  • Familiarity with machine learning and deep learning principles.
  • Knowledge of fundamental computer science concepts including common data structures and algorithms.
  • Ability to collaborate effectively with diverse teams.
  • Excellent English language skills.

  • Develop, refine, and use ML engineering platforms and components.
  • Ensure programs can efficiently handle large volumes of data and meet deadlines.
  • Establish and manage processes for models, including data preparation and prediction.
  • Monitor model performance closely and address any issues promptly.
  • Collaborate closely with client-facing teams to understand their needs and provide technical support.
  • Translate client requirements into straightforward features.
  • Write robust code that is easy to test, maintain, and troubleshoot.
  • Maintain high standards by adhering to guidelines, participating in code reviews, and ensuring code quality.
  • Thoroughly test all components to anticipate and resolve potential issues.
  • Utilize tools for issue tracking, code review, and version control.
  • Actively participate in team meetings to discuss progress and future plans.
  • Stay updated on the latest developments in technology and explore innovative solutions.

AWSPythonGCPKubeflowKubernetesMachine LearningMLFlowAirflowAlgorithmsAzureData engineeringData StructuresSparkCI/CDDevOps

Posted 2 months ago
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