An AI Developer designs, develops, and implements artificial intelligence solutions within software applications. The role focuses on translating business and product requirements into practical AI capabilities using machine learning, deep learning, data processing, and software engineering.

Use this AI Developer job description to define the responsibilities, skills, and qualifications required for the role and to attract candidates who can build reliable AI solutions that support real business needs.

AI Developer Job Summary

The role of an AI Developer involves designing, developing, and implementing AI solutions that help an organization optimize operations, improve products, automate workflows, and solve complex problems. AI Developers typically work with data scientists, software engineers, product teams, and business stakeholders to take AI systems from concept and prototype through deployment and ongoing improvement.

Main Responsibilities of an AI Developer

As an AI Developer, job responsibilities can vary by organization and use case. Below are key responsibilities commonly required for the role:

• Designing and developing machine learning models and algorithms based on business and product requirements.
• Preparing, cleaning, transforming, and validating data used to train and evaluate AI systems.
• Building AI-powered features and integrating models, APIs, and services into software applications.
• Testing and evaluating model performance, reliability, accuracy, and behavior before deployment.
• Deploying AI solutions and supporting model serving, inference, scalability, and production integration.
• Monitoring deployed AI systems and identifying performance degradation, data drift, errors, latency, or other operational issues.
• Troubleshooting and debugging AI models, pipelines, integrations, and application-level issues.
• Collaborating with data scientists, software engineers, product managers, analysts, and other stakeholders.
• Documenting AI solutions, technical decisions, model behavior, assumptions, and implementation requirements.
• Applying appropriate security, privacy, fairness, and responsible-AI practices throughout development and deployment.

Job Brief

We are looking for an AI Developer who can design and implement practical artificial intelligence solutions for real-world use cases. The ideal candidate should be comfortable working across machine learning, software development, data workflows, and production systems, with the ability to turn prototypes into reliable features and applications.

The candidate should understand how to choose suitable AI approaches, evaluate model performance, write maintainable code, collaborate with cross-functional teams, and continuously improve AI systems after deployment.

Responsibilities

• Understand business requirements and translate them into AI or machine learning solutions.
• Develop, train, test, and evaluate machine learning and deep learning models.
• Build and maintain data preprocessing, feature engineering, and model pipelines.
• Integrate AI models and services into applications using APIs and software engineering best practices.
• Work with generative AI, large language models, retrieval-based systems, or other modern AI approaches when relevant to the use case.
• Optimize models and AI applications for accuracy, latency, scalability, maintainability, and cost.
• Deploy and monitor AI solutions in development, cloud, or production environments.
• Investigate model errors and production issues and implement fixes or improvements.
• Collaborate with technical and non-technical stakeholders throughout the development lifecycle.
• Maintain technical documentation and communicate AI capabilities, limitations, and trade-offs clearly.

Requirements

Recruiters seeking to hire AI Developers should look for candidates with a strong foundation in computer science, software engineering, machine learning, or a related technical field. Practical experience and demonstrated skills should be considered alongside formal education.

• Strong programming skills, particularly in Python, with the ability to write clean and maintainable production code.
• Working knowledge of machine learning, deep learning, statistics, data structures, algorithms, and model evaluation.
• Experience with common machine learning or deep learning frameworks and libraries.
• Knowledge of data preprocessing, feature engineering, model training, testing, and validation.
• Familiarity with cloud computing, APIs, databases, version control, and deployment workflows.
• Understanding of generative AI concepts, model APIs, embeddings, retrieval-augmented generation, or related approaches where relevant.
• Ability to evaluate and monitor AI systems using appropriate performance and quality metrics.
• Strong analytical, problem-solving, troubleshooting, and debugging skills.
• Effective communication skills for working with cross-functional teams, including data scientists, engineers, analysts, and business stakeholders.
• Awareness of AI security, privacy, bias, responsible use, and risks associated with deploying AI systems.

Key Skills for an AI Developer

• Machine learning and deep learning
• Python and software development
• Data preprocessing and model evaluation
• Generative AI and LLM application development
• APIs and AI system integration
• Cloud deployment and model serving
• Production monitoring and troubleshooting
• Problem-solving and analytical thinking
• Communication and cross-functional collaboration
• Responsible AI, security, and privacy awareness

How Can Glider AI Help You with Hiring an AI Developer?

Glider’s recruitment platform is built around competency over credentials, helping hiring teams evaluate candidates through structured, skills-based assessments and interviews. For AI Developer roles, this helps recruiters validate practical technical capability instead of relying only on resumes or self-reported experience.

Glider AI’s Unique Features

• Conversational Chatbot for Talent Screening
• On-demand One-way Interviews
• Interactive, coding-enabled skill tests
• Task-based and Live Coding enabled Video Interviews
• Advanced AI proctoring
• AI Assistant for technical assessments and interviews
• Powerful candidate analytics

Discover Hiring Resources for AI Developers

• Hiring an AI Developer
• AI Developer Interview Questions
• AI Developer Skills Test
• How to hire an AI Developer

Go ahead and spotlight your AI Developer with Glider AI today!

You can always write to us at info@glider.ai to help you access the hiring resources.

Conclusion

A strong AI Developer job description should make the role’s expectations clear across machine learning, software development, data, deployment, and collaboration. As AI development increasingly moves from experimentation into production, hiring teams should look for candidates who can not only build models but also integrate, evaluate, monitor, and improve AI systems in real-world environments.

FAQs

What does an AI Developer do?

An AI Developer builds and integrates artificial intelligence functionality into software products and business systems. Typical work includes preparing data, developing and evaluating models, integrating AI services, deploying solutions, and monitoring performance.

What skills are required for an AI Developer?

Common skills include Python, machine learning, deep learning, data processing, APIs, model evaluation, cloud or deployment workflows, problem-solving, and communication. Generative AI and LLM application skills may also be required for modern AI roles.

What is the difference between an AI Developer and a Data Scientist?

AI Developers generally focus more on building, integrating, and deploying AI-powered software, while Data Scientists often focus more on data exploration, experimentation, statistical analysis, and developing models or insights. The responsibilities can overlap depending on the organization.

Should an AI Developer know generative AI and LLMs?

For roles involving generative AI products or workflows, knowledge of model APIs, prompting, embeddings, retrieval, evaluation, and AI application design can be valuable. The exact requirement should depend on the job’s actual use cases.

How should recruiters assess an AI Developer?

Recruiters can combine a structured interview with practical technical assessments, coding exercises, and job-relevant scenarios. This helps evaluate both foundational AI knowledge and the candidate’s ability to build and troubleshoot working solutions.