Valuebound - Senior Machine Learning Operations Engineer

3-5 Years

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Early Applicant

Job Description

Description

Manage Data : Extract, clean, and structure both structured and unstructured data.
Coordinate Pipelines : Utilize tools such as Airflow, Step Functions, or Azure Data Factory to orchestrate data workflows.
Deploy Models : Develop, fine-tune, and deploy models using platforms like SageMaker, Azure ML, or Vertex AI.
Scale Solutions : Leverage Spark or Databricks to handle large-scale data processing tasks.
Automate Processes : Implement automation using tools like Docker, Kubernetes, CI/CD pipelines, MLFlow, Seldon, and Kubeflow.
Collaborate Effectively : Work alongside engineers, architects, and business stakeholders to address and resolve real-world problems efficiently.

Requirements

3+ years of hands-on experience in MLOps (4-5 years of overall software development experience).
Extensive experience with at least one major cloud provider (AWS, Azure, or GCP).
Proficiency in using Databricks, Spark, Python, SQL, TensorFlow, PyTorch, and Scikit-learn.
Expertise in debugging Kubernetes and creating efficient Dockerfiles.
Experience in prototyping with open-source tools and scaling solutions effectively.
Strong analytical skills, humility, and a proactive approach to problem-solving.
Experience with SageMaker, Azure ML, or Vertex AI in a production environment.
Commitment to writing clean code, creating clear documentation, and maintaining concise pull requests.

(ref:hirist.tech)