AI Engineer Job Description Template | Skills & Responsibilities

Craft a compelling AI Engineer job description with key skills, responsibilities, and best practices to attract top AI talents.
Written by
Stellaspire Team
Last updated on
March 18, 2025

Artificial Intelligence (AI) is transforming industries, and the demand for AI Engineers has never been higher. Companies across tech, healthcare, finance, and e-commerce are hiring AI professionals to develop intelligent systems, automate processes, and enhance decision-making.

However, hiring an AI Engineer isn’t as simple as listing programming skills. A well-structured job description (JD) is crucial to attracting the right candidates, setting clear expectations, and streamlining the hiring process.

In this guide, we’ll break down the key components of an AI Engineer job description and how Stellaspire can help you hire top AI talent effortlessly.

What Does an AI Engineer Do?

An AI Engineer designs, builds, and deploys machine learning (ML) and artificial intelligence models that help businesses automate tasks, analyze vast datasets, and improve decision-making.

Their role often includes:

  • Developing AI models for NLP, computer vision, and predictive analytics
  • Training, testing, and optimizing machine learning algorithms
  • Deploying AI solutions using cloud platforms like AWS, Azure, or GCP
  • Ensuring AI fairness, reducing biases, and maintaining ethical AI practices
  • Collaborating with data scientists and software engineers

Leading companies like Google DeepMind, OpenAI, NVIDIA, and JPMorgan Chase AI Lab frequently hire AI Engineers, offering opportunities to work on cutting-edge AI innovations.

Key Components of an AI Engineer Job Description

1. Job Title & Summary

A job title should be clear and aligned with industry standards. Instead of using vague titles like AI Specialist or ML Developer, stick to specific, recognizable ones like:

  • AI Engineer (NLP & Chatbots)
  • Machine Learning Engineer – Deep Learning
  • Computer Vision AI Engineer

The summary should briefly outline the role, core responsibilities, and the impact the engineer will have on the company.

💡 Example:
"We’re looking for an AI Engineer to develop and deploy cutting-edge machine learning models that improve automation, optimize decision-making, and enhance user experiences. You will work on AI-driven projects across NLP, computer vision, and predictive analytics while collaborating with data scientists and software engineers."

2. Core Responsibilities

Clearly define the day-to-day tasks to help candidates understand their role. Avoid generic tasks and instead highlight real, industry-relevant responsibilities:

  • Build, train, and optimize AI models for speech recognition, image processing, or recommendation systems
  • Develop scalable machine learning pipelines for processing massive datasets
  • Deploy AI models in production using Docker, Kubernetes, and cloud AI services
  • Collaborate with product teams to integrate AI into applications
  • Monitor model performance and implement continuous improvements

Best Practice: Use actionable, skill-based responsibilities instead of broad statements like “Work with AI technologies.” 

3. Required Skills & Qualifications

This section must be specific and tailored to attract candidates with the right technical expertise.

Must-have skills:

  • Programming: Python, Java, or C++
  • Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn
  • Cloud & MLOps: AWS SageMaker, Azure ML, Google AI Platform
  • Big Data Tools: Hadoop, Spark (for large-scale AI models)
  • Mathematics & Algorithms: Linear algebra, probability, optimization

4. Preferred Qualifications (Nice-to-Have)

This section helps filter top talent while giving candidates an idea of additional skills that can make them stand out.

  • Experience in NLP, computer vision, or reinforcement learning
  • Prior work in AI ethics and bias reduction
  • Exposure to big data technologies like Apache Spark
  • Contributions to open-source AI projects

5. Salary Range & Job Outlook

Compensation: Do detailed industry research to understand what the current market rate is and then offer something feasible to both parties. Companies should conduct market benchmarking to remain competitive.

List further incentives or company benefits like PTOs, health insurance, stock options, or other benefits the candidate can leverage. 

How Stellaspire Can Help You Hire AI Engineers

Hiring AI talent requires more than just a well-written JD—it demands a targeted recruitment strategy to identify professionals with strong AI expertise and real-world experience.

Stellaspire, a women-led recruitment consultancy, specializes in connecting organizations with highly qualified talent. We focus on mid to senior executive placements and offer tailored support to meet your hiring goals.

Stellaspire’s approach includes advanced technical screening by industry experts and access to a global network of pre-vetted candidates. Our consultative services can help your organization improve hiring efficiency, increase interview-to-hire ratios, and build a stronger, more diverse leadership pipeline. 

Reach out for expert guidance, tailored recruitment solutions, and access to a global network of top-tier talent.

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