AI Engineer Job Description

About {{Company Name}}

Pro Tip: Be sure to mention your company’s AI ambitions here if they are a differentiator.

{{Company Name}} is a {{startup/growing company/enterprise organization}} in the {{your industry}} space focused on {{brief description of your product/service/what you’re building}}. Our team of {{add team size or a descriptor like “experienced researchers”}} is dedicated to helping {{target customers/industry}} achieve {{main benefit/goal}}.

At {{Company Name}}, we value {{mention 2 or 3 cultural values, collaboration/innovation/transparency/ownership}}. We offer competitive benefits including {{add 1 or 2 high-level benefits, flexible working hours/remote work opportunities/health benefits}}.

Our goal is to create an environment where talented professionals can grow their careers while working on meaningful, high-impact projects. We work in a {{remote/hybrid/in-office}} environment and encourage a culture of {{continuous learning/experimentation/autonomy, etc.}}.

We’re looking for a talented {{junior/mid-level/senior}} artificial intelligence engineer to join our team and help shape the future of {{our product/platform/industry solution}}.

AI Engineer Job Description: Role Overview

We’re searching for a motivated, results-driven AI engineer to help us build {{systems/products/features}} that solve real challenges for {{our customers/our business/our platform}}.

Note: The next paragraph clarifies your AI engineer hiring intent. Choose the example section that best matches your job seniority needs and update the specifics.

{{junior}} In this junior role, you will support the deployment of existing AI models and assist in data preprocessing and model monitoring.

{{mid-level}} In this mid-level role, you will take end-to-end project ownership, deploy AI models at scale, and manage and optimize model performance.

{{senior}} In this senior role, you will lead the AI architecture, mentor junior developers, and define the long-term AI roadmap for the organization.

You’ll work closely with data scientists, back-end developers, and product managers to move projects from prototype to production and transform our AI strategy.

This position requires strong collaborative and communication skills, as you’ll be expected to contribute to strategy discussions and translate complex technical concepts for nontechnical stakeholders.

Role Details

• Contract type: {{Full-time/Part-time/Contract}}
• Work arrangement: {{Remote/Hybrid/On-site}}
• Location: {{City/Country}}
• Salary range: {{$XXX,XXX-$XXX,XXX per year depending on experience.}}
• Benefits: {{Add your detailed benefit list here, such as: flexible working hours, remote work opportunities, professional development support, health benefits, 401K match, life insurance, stock options, PTO, and others that apply.}}

AI Engineer Responsibilities

Note: Select a focused list of the responsibilities most relevant to your project. Add others specific to your project, and remove anything that doesn’t apply. Ten or fewer is suggested.

Common Core Responsibilities

• Model development: Design, train, and deploy machine learning, deep learning, and large language models for production
• Data engineering: Build and manage scalable data pipelines and preprocessing workflows to aid in model development
• Integration: Incorporate AI functionality into existing web or mobile applications via APIs
• Optimization: Implement model training, validation, testing, and performance evaluations
• Collaboration: Work with cross-functional teams to define AI-driven features, optimize technical viability, and align with user requirements

LLM/Generative AI Responsibilities

• Develop and optimize prompt engineering for large language models (LLMs)
• Design and maintain retrieval-augmented generation (RAG) pipelines with vector databases
• Fine-tune pretrained models using LoRA, QLoRA, or SFT methods

Computer Vision Responsibilities

• Build and deploy image recognition and object detection systems
• Work with convolutional neural networks (CNNs) and vision transformers
• Optimize models for real-time processing

NLP Responsibilities

• Develop natural language processing systems (classification, summarization)
• Work with embeddings, tokenization, and semantic search

MLOps/Production AI Responsibilities

• Build CI/CD pipelines for machine learning models and life-cycle management
• Set up workflows to monitor model drift and performance in production
• Implement experiment tracking and manage model versioning with MLflow, Weights & Biases, or DVC

{{Add other relevant responsibilities here.}}

AI Engineer Skills and Qualifications

Note: Select the core skills most relevant to your role and add on as needed. Be sure to mention other frameworks, libraries, or technologies related to your development stack.

• Strong proficiency in Python, Java, R, C++, or other machine learning programming languages
• Solid experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn
• Experience with natural language processing and/or computer vision applications
• Competence with cloud platforms such as AWS, Microsoft Azure, or Google Cloud
• Experience with containerization and deployment tools: Docker, Kubernetes, or similar
• Knowledge of data preprocessing, feature engineering, and model evaluation techniques
• Familiarity with MLOps practices and model deployment pipelines using tools like SageMaker Pipelines or Vertex Pipelines
• Practiced ability to analyze large, complex datasets and extract actionable insights
• Solid knowledge of statistics, probability, and algorithm design such as the Simplex method, Levenberg-Marquardt, Newton’s method, etc.
• Strong understanding of linear algebra and advanced mathematics
• Good ability to communicate complex technical concepts to nontechnical stakeholders
• Collaborative mindset with experience working in cross-functional teams

Nice-to-Have Qualifications

Note: Select the most relevant qualifications, add on as needed, and remove what doesn’t apply.

The following qualifications are not required, but would be considered a strong advantage:

• Knowledge of AI safety, alignment, or responsible AI frameworks
• Experience with experiment-tracking platforms: MLflow, Weights & Biases, or Neptune
• Familiarity with workflow orchestration tools: Apache Airflow, Prefect, Kubeflow, or Dagster
• Experience with large-scale data processing frameworks: Apache Spark, Ray, or Dask
• Familiarity with data warehousing tools: Snowflake, BigQuery, or Redshift
• Experience leading a small engineering team or serving as a technical lead on a project
• Previous experience in {{add industry specifics, such as healthcare/life sciences, fintech/banking, transportation/autonomous vehicles, retail/e-commerce}}
• Strong track record of presenting technical work to executive or nontechnical audiences

Education and Certification Requirements

Pro Tip: Depending on the role, you may consider a “skills-first” evaluation instead of filtering by education and certifications. Practical, hands-on experience with emerging technologies can be more valuable than traditional credentials.

• A bachelor’s degree in computer science, data science, engineering, or a related field is preferred but not required
• Advanced AI degrees or relevant AI certifications in machine learning, cloud technologies, or related disciplines are considered an asset
• Practical experience with a strong, verifiable portfolio is valued

Experience

Note: Adjust the experience based on your project’s needs. For a junior role, emphasize learning, contribution, and execution. For a more senior role, emphasize ownership, leadership, and strategic input.

• Junior AI engineer: 1-2 years of experience or relevant internships/projects
• Mid-level AI engineer: 2-5 years of hands-on experience building and deploying AI systems
• Senior AI engineer: 4-7+ years of experience with a proven track record of developing AI projects and deploying production-grade models at scale

Apply Now

Pro Tip: A clear, compelling call to action helps candidates understand how to reach out to your team and what they should share. This is an opportunity to make a final impression about your company’s culture and care for candidates, rather than immediately moving to an Apply button.

If you’d like to {{make a difference/solve problems that matter/help us scale}} we’d love to hear from you! Send your {{resume/cover letter/portfolio/GitHub review}} to {{email/link /recruiter name}} by {{date}} and let us know what interests you about this role.

{{Company Name}} is an equal opportunity employer. We welcome applications from candidates of all backgrounds and are committed to building a diverse and inclusive team.