ML Engineer - AI-First Systems, Remote

Interview Kickstart

United States

Remote

USD 150,000 - 230,000

Full time

14 days+

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Benefits offered by this job

Permanent remote work

Job summary

Interview Kickstart is seeking an AI-focused professional to tackle real-world problems using statistical modeling, ML, and generative AI/LLMs. You will design and implement AI-powered solutions, including agent systems with LangChain and various APIs, and collaborate with product teams to drive learner outcomes.

You will deploy these models in scalable production environments on cloud platforms, stay current with AI advancements, and contribute to robust evaluation frameworks.

Qualifications

  • Proven experience building statistical and ML models across traditional and deep learning methods.
  • Hands-on experience with AI agents and multi-step workflows.
  • Strong prompt engineering and practical deployment skills.

Responsibilities

  • Collaborate with product managers and stakeholders to translate business challenges into ML problems and actionable requirements.
  • Research and design ML solutions using statistical modeling and generative AI/LLMs.
  • Work with ML engineers to develop, refine, and deploy AI solutions in production on cloud platforms (AWS/Azure/GCP).
  • Build and evaluate agent pipelines, tests for agent behaviors, and evaluation frameworks.

Skills

Statistical modeling
Machine learning
Generative AI/LLMs
Prompt engineering

Tools

LangChain
LangGraph
OpenAI API
Gemini API
Anthropic API
Pinecone
Weaviate
AWS/Azure/GCP

Job description

Interview Kickstart is seeking an AI-focused professional to tackle real-world problems using statistical modeling, ML, and generative AI/LLMs. You will design and implement AI-powered solutions, including agent systems with LangChain and various APIs, and collaborate with product teams to drive learner outcomes.

You will deploy these models in scalable production environments on cloud platforms, stay current with AI advancements, and contribute to robust evaluation frameworks.

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