AI/ML Engineer Job Description: A Comprehensive Guide (2026)

Summary: AI/ML engineers are increasingly needed as companies use automation, predictive models, and generative AI in their products and operations. Since the role can involve model development, deployment, or maintenance, a clear job description helps employers attract candidates with the right skills and experience.

Hiring an AI/ML engineer requires more than listing a job title and technical requirements. Candidates need to understand the responsibilities, technologies, experience level, and expectations associated with the role before applying. A clear job description also gives recruiters specific criteria for evaluating applications and helps attract candidates whose skills align with the role. For employers, defining the role accurately can make screening more efficient and reduce applications from candidates who do not meet essential requirements. 

This guide provides a practical AI/ML engineer job description, covering key responsibilities, qualifications, skills, and other details employers should include.

What Does an AI/ML Engineer Do?

These professionals develop and maintain systems and applications that use artificial intelligence and machine learning to solve specific business or technical problems. Depending on the organization and project, an AI/ML engineer may prepare data, develop machine learning models, productionize them, integrate them into applications, and make sure they continue to perform reliably after deployment. 

Their work may also involve cloud infrastructure, model serving, APIs, and MLOps practices that support testing, deployment, versioning, monitoring, and maintenance. Some roles may focus on areas such as natural language processing, computer vision, recommendation systems, or generative AI and large language models (LLMs).

The exact responsibilities vary by company and seniority. An AI/ML engineer might be responsible for some or all of the following:

• Collecting, cleaning, and preparing data for machine learning models.
• Building and training machine learning models using programming languages and AI frameworks.
• Productionizing models and integrating them into software applications or APIs.
• Testing and evaluating model performance to improve accuracy.
• Monitoring model performance, reliability, latency, and resource usage after deployment.
• Deploying AI models into production and monitoring their performance.
• Working with software developers, data scientists, and business teams to develop AI-powered applications.
• Optimizing existing models based on new data and changing business requirements.
• Working with generative AI, LLMs, or other specialized AI technologies when required by the project.
• Documenting models, workflows, and technical processes to support future development.

AI ML Engineer Job Description Sample

The following AI ML engineer job description sample can be helpful if you are looking to hire AI/ML talent. You can customize the responsibilities, required skills, qualifications, salary, and benefits to match your company’s needs and the complexity of the role. 

Job Title: AI/ML Engineer Company: NovaCart Technologies Location: Pune, Maharashtra Employment Type: Full-Time Experience: 2 – 4 years About the Company NovaCart Technologies is an e-commerce technology company that builds digital solutions for online retailers. Our product engineering teams use machine learning to improve product discovery, personalize customer experiences, and automate business processes. We are looking for an AI/ML Engineer to contribute to our product recommendation and search systems. About the Role As an AI/ML Engineer, you will join our engineering team and work with data scientists, backend developers, and product managers to improve product search and recommendation features. You will be responsible for developing and deploying models, analyzing their performance, and helping integrate ML capabilities into our platform. The role offers ownership of projects from experimentation through production deployment. Key Responsibilities – Develop and evaluate machine learning models for product recommendations and search. – Work with structured and unstructured datasets to identify useful patterns and features. – Experiment with different modeling approaches and evaluate their effectiveness. – Collaborate with backend engineers to integrate models into production applications. – Monitor deployed models and investigate performance or data-quality issues. – Improve existing models based on user behavior and product performance data – Maintain documentation for experiments, models, and deployment processes Required Qualifications and Skills – Bachelor’s or master’s degree in Computer Science, Data Science, Engineering, or a related field – 2- 4 years of experience in machine learning or a related engineering role – Strong programming skills in Python – Good understanding of machine learning algorithms and statistics – Experience with libraries or frameworks such as scikit-learn, PyTorch, or TensorFlow – Working knowledge of SQL and data manipulation – Familiarity with Git and software development workflows – Understanding of model evaluation and feature engineering Preferred Skills – Experience working with recommendation systems or search technologies – Familiarity with cloud platforms such as AWS, Azure, or Google Cloud – Knowledge of Docker and basic MLOps practices – Experience working with NLP or LLM-based applications – Exposure to deploying ML models in production environments Compensation and Benefits CTC: ₹14 LPA – ₹18 LPA, based on experience and relevant skills Benefits: – Health insurance – Paid leave – Performance-linked incentives – Professional development support – Learning and certification opportunities – Hybrid work arrangement Work Schedule: – Full-time – Monday to Friday – Hybrid work model – Pune office How to Apply? Candidates can submit their updated resume through the company’s careers page. You may also include links to relevant projects, publications, or publicly available code. Candidates shortlisted after the initial screening will be invited to a technical assessment followed by interviews with the hiring team.

You Know: AI/ML hiring in India is surging: up 54% in early 2026 and 88% year-on-year in 2025, with around 185,000 active AI/ML openings as of January 2026.

AI ML Engineer Job Description: Key Elements

A clear job description helps candidates understand the role and decide whether they are a good fit. It also helps employers attract qualified applicants and makes the hiring process more efficient. Including the following sections in your AI or machine learning engineer description makes your job post clear, complete, and easier to understand: 

1. Company Overview

Start with a brief introduction to your company. It gives candidates an idea of your business, industry, products or services, and work environment. Keep this section short and focus on information that helps candidates understand where they will work.

You can also mention how your company uses AI or machine learning. For example, explain whether the team develops predictive models, recommendation systems, automation tools, or AI-powered products. 

Include details such as:

• Company name and industry
• Products or services
• How the company uses AI or machine learning
• Company mission or values
• Team culture and growth opportunities

For example:

Company Overview: ‘XYZ Technologies is a software company that develops AI-powered solutions for the healthcare industry. Our engineering team builds machine learning applications that help healthcare providers analyze data and improve decision-making. We use cloud-based infrastructure and MLOps practices to support the deployment and monitoring of our models. We offer a collaborative environment where engineers work with data scientists, software developers, and product teams on practical AI projects.’

2. Job Overview

The job overview gives candidates a clear picture of what the AI/ML engineer will be responsible for and how the role contributes to the business. Since the title can cover different types of work across organizations, avoid describing the role only with broad phrases such as ‘develop AI solutions.’ State the specific product, system, or business problem the engineer will work on. Keep it concise, ideally between three and five sentences.

Include in this section:

• The team the engineer will join and who they will report to
• The specific product, system, or business problem they will work on
• The main type of AI/ML work involved, such as model development, productionizing models, recommendation systems, fraud detection, or predictive analytics
• The seniority level and expected level of ownership
• The expected impact of the role on the product or business

For example:

About the Role: ‘We are looking for a mid-level AI/ML Engineer to join our product engineering team and report to the Engineering Manager. The role will focus on developing and deploying machine learning models for our customer recommendation platform, with an emphasis on improving recommendation accuracy and user engagement.  You will work closely with data scientists, software engineers, and product managers to take models from experimentation to production. The engineer will own model development and optimization and contribute to improving the overall performance of the recommendation system.’

3. AI ML Engineer Roles and Responsibilities

The responsibilities section should show candidates what they will actually work on and what they will be expected to deliver. Use specific, action-oriented bullet points that reflect the role rather than copying a long list from a generic AI/ML job description. 

Note that the responsibilities will vary depending on the product, team, and seniority of the position. So, employers should select responsibilities that match the actual position. Avoid adding every AI technology simply to make the job description appear more comprehensive.

For example:

Roles & Responsibilities: – Develop, test, and optimize machine learning models for the company’s recommendation platform. – Prepare and analyze datasets to identify patterns that can improve recommendation accuracy and user engagement. – Deploy trained models into production and monitor their performance, reliability, and response times. – Collaborate with data scientists, software engineers, and product managers to integrate ML models into existing product features. – Identify model performance issues and improve models through feature engineering, experimentation, and tuning. – Build and maintain data and model pipelines that support reliable model training and deployment. – Document model development, experiments, deployment processes, and key technical decisions for the wider engineering team.

4. AI ML Engineer Skills and Qualifications

This section should clearly distinguish between the skills candidates must have and those that are preferred for the specific role. Avoid treating every AI/ML technology as a standard requirement. The right skill set depends on the team’s technology stack, the product being developed, and the level of the position.

For example:

Qualifications – Degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field, where relevant to the role – Demonstrable experience developing or working with machine learning solutions – Strong understanding of machine learning fundamentals, algorithms, and model evaluation – Relevant certifications in AI, machine learning, cloud computing, or related technologies (preferable) – Experience working on production AI/ML systems or relevant personal and academic projects (preferable) Skills Requirements – Proficiency in Python and familiarity with Java or C++ where relevant to the team’s stack – Strong understanding of machine learning algorithms and core statistics – Experience with at least one ML framework, such as TensorFlow, PyTorch, or Scikit-learn – Knowledge of SQL and database management – Experience with Git and version control workflows – Understanding of data preprocessing, feature engineering, and model evaluation Preferred Technical Skills (Based on Role and Team Stack) – Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud – Familiarity with MLOps tools and practices, including Docker, CI/CD pipelines, and model monitoring – Experience with Kubernetes or similar tools for managing deployed models at scale – Exposure to GenAI or LLM technologies, including prompt engineering, fine-tuning, or RAG systems – Background in NLP, computer vision, or deep learning, depending on the product area

Pro Tip: Employers should review their actual technology stack before adding tools or platforms to the job description. Keeping the requirements specific helps candidates understand what the role actually demands.

5. CTC, Benefits, and Work Schedule

Include salary, benefits, and work schedule details to help candidates understand the complete job offer. Being transparent about compensation and work arrangements in the AI ML job description can attract more suitable applicants and reduce mismatched expectations.

You can include:

• CTC or salary range
• Performance bonuses or incentives
• Health insurance and paid leave
• Learning and certification support
• Flexible, remote, hybrid, or on-site work options
• Working days and hours

For example:

CTC: ₹12 LPA to ₹15 LPA Benefits: – Health insurance – Paid leave and public holidays – Performance-based bonuses – Learning and certification support – Flexible work options or hybrid work – Employee wellness programs Work Schedule: – Full-time position – Monday to Friday – Hybrid or work-from-office (based on business requirements)

6. Call-to-Action (CTA)

End the AI ML engineer job description with clear instructions on how candidates can apply. Mention where they should submit their application, what documents are required, and what they can expect after applying. 

Do not make GitHub mandatory for every AI/ML role. Candidates may have relevant professional experience or projects that cannot be shared publicly, especially when they have worked with confidential company data or proprietary systems.

You can ask candidates to submit:

• An updated resume or CV
• Relevant AI/ML project links or portfolio
• GitHub profile, if they have relevant projects or code to share
• Certifications or other supporting documents, where relevant
• A cover letter, if required for the role

You can also mention whether shortlisted candidates will complete a technical assessment, coding task, or interview.

For example:

How to Apply? ‘Interested candidates can send their updated resume and relevant project or portfolio links to careers@xyztech.com with the subject line ‘Application for AI/ML Engineer.’ If you have relevant work on GitHub, you can include your profile link with your application. Shortlisted candidates will be contacted for a technical assessment followed by interview rounds.’

Tips for Writing an Effective AI ML Engineer Job Description

A well-written job description helps you attract qualified AI ML engineers and reduces irrelevant applications. Keep the content clear, specific, and focused on the actual requirements of the role. Follow these tips to make your AI machine learning engineer job description more effective: 

• Use a Clear Job Title: Choose a job title that candidates commonly search for, such as AI ML Engineer, Machine Learning Engineer, or AI Engineer. Avoid creative titles that may reduce your job posting’s visibility.
• Define the Business Problem: Explain what the engineer will actually work on. Mention the product, system, or business problem they will help solve, such as fraud detection, recommendations, forecasting, or an AI-powered application.
• Separate Required and Preferred Skills: Clearly identify the skills candidates must have and those that would be an advantage. This helps prevent qualified candidates from ruling themselves out because they do not meet every preferred requirement.
• Avoid Mentioning Unrealistic Technology Wish Lists: Do not make every AI technology a requirement. Skills such as MLOps, cloud platforms, Kubernetes, NLP, computer vision, LLMs, or RAG should only be included when they are relevant to the actual role.
• Mention the Tech Stack: Tell candidates which programming languages, frameworks, cloud platforms, deployment tools, or other technologies they will use. This helps them assess whether their experience matches the position.
• Specify the Work Model: Clearly state whether the role is remote, hybrid, or office-based. If the role is hybrid or office-based, include the location and any expected office days.
• Be Clear About Salary and Benefits: Include the salary or CTC range, key benefits, and other relevant compensation details. It helps candidates understand the offer before they apply.
• Keep the Application Process Clear: Explain how candidates should apply, what they need to submit, and what the hiring process involves. If you plan to use a technical assessment, coding task, or interview stage, mention it in the job description.
• Mention Salary and Benefits: Include the salary range, work model, benefits, and learning opportunities. This helps candidates decide whether the role matches their expectations.
• Keep the Application Process Simple: Explain how candidates should apply, what documents they need to submit, and the hiring process. Clear instructions improve the candidate experience and encourage more qualified professionals to apply.

Conclusion

A clear AI ML engineer job description helps you attract qualified candidates and makes the hiring process more efficient. Instead of relying on a generic AI/ML template, customize the job description around the actual responsibilities, technology stack, seniority level, and business problems the engineer will handle. Define the role, responsibilities, required skills, qualifications, salary, and application process clearly so candidates know what to expect. You can use the sample and tips in this guide as a starting point and customize them to match your company’s hiring needs. 

Looking to hire for other technical roles? Read our guide on the software engineer job description to learn how to write a clear job posting and attract qualified developers for your organization. 

FAQs

Sources:

• https://www.techlifeadventures.com/post/ai-talent-war-india-hiring-companies-2026
• https://www.linkedin.com/posts/anish-shandilya_the-job-market-for-ai-tech-aiml-hiring-share-7431935550896431104-qerf/