Remote Junior AI/ML Engineer: Build Practical AI Apps

Drivenintelligence; United States; Hybrid; USD 75,000 - 120,000; Full time

Job summary

Drivenintelligence in the United States is seeking a junior AI/ML Engineer to prototype and implement LLM-powered workflows and AI applications. You will build prototypes using Python, orchestrate retrieval systems, and collaborate with business stakeholders to translate requirements into practical tools.

You will work with data engineers, adopt best practices, and document model behavior, results, and decisions while growing hands-on skills in NLP, MLOps, and deployment.

Qualifications

• Hands-on Python experience with a basic understanding of ML concepts.
• Exposure to LLMs, embeddings, retrieval-augmented generation, or AI application development.
• Ability to test, evaluate, and improve outputs rather than treating model responses as magic.
• Familiarity with APIs, Git, and modern software development workflows.
• Curiosity, humility, and a drive to build practical AI systems for business users.

Responsibilities

• Build AI application prototypes using Python, LLM APIs, retrieval systems, and workflow tools.
• Support data preparation, embedding workflows, prompt design, and evaluation runs.
• Help turn proof-of-concept AI workflows into reliable applications and internal tools.
• Collaborate with data engineers, adoption leads, and business stakeholders to clarify requirements.
• Document model behavior, limitations, test results, and implementation decisions.

Skills

Python

ML concepts

LLMs

Embeddings

APIs & Git

Education

NLP/ML coursework

Tools

LangChain

LlamaIndex

OpenAI APIs

Vector databases

Git

Job description

Drivenintelligence in the United States is seeking a junior AI/ML Engineer to prototype and implement LLM-powered workflows and AI applications. You will build prototypes using Python, orchestrate retrieval systems, and collaborate with business stakeholders to translate requirements into practical tools.

You will work with data engineers, adopt best practices, and document model behavior, results, and decisions while growing hands-on skills in NLP, MLOps, and deployment.