P1
AIMLAM.GEN.P1
Entry-Level AI/ML Engineer

JobFrame · AIMLAM.GEN.P1

Artificial Intelligence / Machine Learning (AI/ML) Engineer · General

P1 · P1 — Entry-Level Professional · Individual contributor

Median pay · United States

$61,862

$48,598$78,747 · USD · annual · national base (function pricing in review)

Level position

P1 · 1 of 4 in track

Median pay

$61,862

$48,598–$78,747

Level

P1

P1 · 1 of 4 in track

Super-function

technology

Demand-heat

cool

10.1% growth

Summary

P1 is an entry-level position for AI/ML, often a recent graduate or someone with less than 2 years of experience. They require close guidance and mentorship to perform tasks.

This level — P1 P1 — Entry-Level Professional

New to role or field; performs basic tasks under supervision

Who does this work

An AI/ML Engineer who strives to push the boundaries of technology and create intelligent applications that positively impact industries and everyday life.

The problem this role solves

Organizations struggle to leverage the vast amounts of data they collect, leading to missed opportunities for optimization and innovation. They feel overwhelmed by the complexity of data analysis and the challenge of implementing effective AI/ML solutions. In a world driven by technology, the ability to harness data science is not just a benefit; it's a necessity for progress and competitiveness.

The transformation

Organizations make data-driven decisions that significantly improve productivity and efficiency. Innovative solutions lead to enhanced customer experiences and new revenue streams.

What's at risk

The project fails to address the real needs of the organization, leading to wasted resources. The developed solutions are not scalable or adaptable, resulting in stagnation and missed opportunities.

How the role wins

  • Assess the specific needs and challenges faced by the organization.
  • Utilize statistical modeling and machine learning techniques to analyze data and extract valuable insights.
  • Develop and implement self-learning applications that can adapt and improve over time.
  • Collaborate with cross-functional teams to ensure solutions align with business objectives.
  • Monitor and refine models continuously to ensure they remain effective and relevant.
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