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Machine Learning Engineer

Splunk

Updated on: 17 August 2024

Additional Details

Website

www.splunk.com

website

Work Location

Hyderabad, India

location

Job Type

FTE

job_type

Batch

Experience

batch

Stream Required

stream

Salary

16 - 22 LPA (Expected)

salary

Job Description

As a Machine Learning Engineer in the Artificial Intelligence group, you will be responsible for developing the core AI/ML capabilities to power the entire Splunk product portfolio and help our customers to drive their journey to digital resiliency. You will collaborate with cross-functional teams, mentor junior team members, and help drive the engineering roadmap of the area.

Responsibilities:

The responsibilities of this role include:

  • Participate in the development of the AI/ML platform and infrastructure that drives our product’s key ML use cases in the cybersecurity and observability domains under the guidance of senior team members.
  • Assist in collecting, cleaning, and preprocessing data to prepare it for analysis and modeling.
  • Collaborate closely with software engineers, applied scientists, and product managers to integrate generative AI solutions into our products and services.
  • Stay up to date with the latest developments in the field of AI/ML, and ensure that these advancements are accurately incorporated into our technology roadmap.
  • Actively participate in cross-functional discussions and strategic decisions related to AI directions and product roadmaps.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field with at least 2+ years of industry experience.
  • Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
  • Strong knowledge of version control systems, especially Git.
  • Strong knowledge of CI/CD principles and tools.
  • Familiarity with cloud platforms (AWS, GCP, Azure) and serverless architecture.
  • Experience with MLOps platforms such as MLflow or Kubeflow.
  • Previous experience working in cross-functional teams and collaborating with data scientists and DevOps teams.
  • Excellent problem-solving skills and the ability to troubleshoot complex issues.
  • Excellent communication skills with the ability to articulate technical concepts to both technical and non-technical audiences.

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