AI Engineer vs ML Engineer: Roles Compared

AI engineers integrate existing models into products and ship features such as chatbots, copilots, search, and workflow automation. ML engineers build, train, deploy, and monitor machine learning models and pipelines using proprietary data.

Hire an AI engineer first when you want to add features using existing models. Hire an ML engineer first when you have proprietary data and a measurable prediction, ranking, recommendation, fraud, or forecasting target.

Quick comparison

Howdy is a nearshore hiring platform that sources both AI and ML engineers from Latin America. The comparison below is designed to help you choose a role based on your product, data, and infrastructure needs.

What is an AI engineer?

An AI engineer applies existing AI models to product problems. The role usually focuses on building and operating AI features rather than conducting model research. Many companies call this role “LLM engineer” or “Applied AI engineer.”

Core responsibilities

• Integrate LLM APIs (OpenAI, Anthropic, Cohere) into applications
• Build AI-powered features like search, summarization, and chat
• Design prompt templates and orchestration workflows
• Monitor costs, latency, and reliability
• Automate internal operations with AI tools

Common tools

• Python or TypeScript
• LangChain, LlamaIndex, or custom orchestration
• Vector databases (Pinecone, Weaviate, Chroma, Postgres + pgvector)
• Cloud platforms (AWS, GCP, Azure)
• Observability tools (LangSmith, Helicone)

Example projects

• Customer support chatbot that routes tickets and suggests responses
• AI search that retrieves and summarizes internal documentation
• Code review assistant that flags security issues
• Sales email generator that personalizes outreach at scale

AI engineers typically do not train foundation models. They may fine-tune smaller models or manage retrieval-augmented generation systems and evaluations.

What is an ML engineer?

An ML engineer builds and operates production machine learning systems, training, evaluating, deploying, and monitoring models on proprietary data. The role sits between data engineering and applied research.

Core responsibilities

• Design, train, and evaluate ML models on proprietary data
• Build data pipelines and feature engineering systems
• Deploy models to production with monitoring
• Optimize model performance, accuracy, and inference speed
• Maintain model versioning and experiment tracking

Common tools

• Python (primary language)
• PyTorch or TensorFlow for deep learning
• scikit-learn for classical ML
• Apache Spark for distributed processing
• MLflow, Weights & Biases for experiment tracking
• Kubernetes for model deployment

Example projects

• Recommendation engine trained on user behavior data
• Fraud detection model for financial transactions
• Demand forecasting system using time-series data
• Computer vision model for quality control in manufacturing

ML engineers can train models on proprietary data when an off-the-shelf model does not meet a defined performance target.

Key differences

Scope of work

AI engineers work at the application layer. They connect pre-trained models to product features, focusing on user experience, reliability, evaluation, and cost management. ML engineers work at the model layer. They build training/evaluation pipelines on proprietary data, deploy models, and monitor performance, drift, and inference efficiency.

Which one should you hire?

Hire an AI engineer when the main deliverable is an application feature built with an existing model. Hire an ML engineer when the main deliverable is a proprietary model or the data and training pipeline needed to operate it.

• Hire an AI engineer to deploy LLM-based product features such as chat, search, summarization, copilots, and workflow automation.
• Hire an AI engineer to test demand quickly before investing in custom training infrastructure.
• Hire an ML engineer to build a training pipeline around proprietary product, transaction, or event data.
• Hire an ML engineer to improve a measurable prediction, ranking, recommendation, fraud, or forecasting metric.
• Hire both when an AI engineer needs to turn model capabilities into product features while an ML engineer owns training, evaluation, deployment, and model performance.

What to hire if you have little or no usable data

Your available data should determine the first hire. An AI engineer can work with existing documents and workflows, while an ML engineer needs usable data and a defined modeling objective.

If you have strong internal documentation but limited structured data

Hire an AI engineer first. They can build retrieval-augmented generation features over an existing knowledge base of documents, tickets, wikis, or PDFs without waiting for a model-training platform.

Good first projects

• Internal AI search over policies, runbooks, and product docs
• Support agent assist (suggested replies + citations)
• Sales/CS enablement copilots grounded in approved content

If your CRM, product, and support data lack reliable pipelines

Hire a data engineer (or an ML engineer with strong data engineering skills) first. Before training models or doing high-quality personalization, you need consistent event tracking, clean tables, and access controls.

What the data foundation should include

• A single source of truth for key entities (users, accounts, tickets, events)
• Basic ETL/ELT pipelines and data quality checks
• Clear permissions for sensitive fields (PII, financial, healthcare)

If privacy or regulatory requirements limit third-party AI APIs

For LLM features, hire an AI engineer with security and compliance experience to implement data controls, redaction, vendor reviews, and an approved deployment model. Add an ML engineer if the product later requires custom training on restricted data.

If the goal is predictive modeling for fraud, risk, or forecasting on restricted data, hire an ML engineer with strong data engineering support earlier.

If you have high-volume product or event data and a clear metric

Hire an ML engineer first. If you can define a measurable target (conversion lift, churn reduction, ranking quality, fraud rate), an ML engineer can build baselines and iterate toward real performance gains.

Typical first projects

• Ranking/personalization baselines
• Forecasting and anomaly detection
• Propensity models tied to revenue outcomes

Start with an AI engineer when the first deliverable is workflow automation or knowledge access. Start with an ML engineer when the first deliverable must improve predictive performance on proprietary event data.

Howdy can help you assess which role fits your product and data requirements. Book a demo with Howdy to discuss the first hire and team structure.

Salaries and costs

United States in 2026

The following US figures were listed on Indeed in January 2026.

AI engineer:

• Average base salary: $149,079
• Range: $89,653–$247,894
• Top markets: San Francisco ($187,728), New York ($183,450), Cambridge ($163,319)

ML engineer:

• Average base salary: $183,428
• Range: $109,668–$306,796
• Top markets: San Francisco ($214,584), Seattle ($201,694), New York ($197,816)

The cited Indeed averages put ML engineer base pay about 23% above pay in its AI developer category. The pages do not establish why the averages differ, and title definitions, seniority, location, and employer mix may affect the comparison.

Estimated total US employer cost

• AI engineer based on the Indeed AI developer average. $149K salary plus an estimated $62K in benefits and taxes equals $211K annually.
• ML engineer. $183K salary plus an estimated $76K in benefits and taxes equals $259K annually.

Nearshore Latin America benchmark

Howdy groups AI engineer and ML engineer pricing under the same data science and ML/AI benchmark, since both roles draw from the same LatAm talent pool. According to Howdy's 2026 salary data, all-inclusive pricing for data science, ML, and AI roles by seniority is as follows.

AI engineer and ML engineer, all-inclusive by seniority:

• Junior: $65K–$81K
• Mid-level: $89K–$105K
• Senior: $106K–$155K
• Staff: $155K–$188K
• Principal: $188K–$236K

These figures include salary, benefits, and the platform fee. Country and English proficiency shift final pricing within each band, but seniority is the primary driver.

Howdy's benchmark uses one shared band for AI and ML roles at each seniority level. Title alone does not change the price. What changes it is the seniority tier and the specific skill requirements of the role.

Estimated cost difference

• AI engineer based on the Indeed AI developer category. Comparing $211K with a senior nearshore band of $106K to $155K produces an estimated cost reduction of 27% to 50%.
• ML engineer. Comparing $259K with a senior nearshore band of $106K to $155K produces an estimated cost reduction of 40% to 59%.

Latin American nearshore locations can provide working-hour overlap with US companies across UTC-3 to UTC-6. English proficiency varies by candidate and should be assessed during hiring.

When to hire an AI engineer

Hire an AI engineer if your company needs to:

• Ship AI features quickly. You want to launch a chatbot, AI search, or automation tool in weeks, not months. AI engineers integrate pre-built models without training infrastructure.
• Validate product-market fit. You're testing whether users actually want AI features. Building custom models before PMF wastes resources.
• Automate repetitive operations. Customer support, data entry, content generation, and internal tools benefit from LLM integration without custom model training.
• Use an existing model instead of funding custom training. An AI engineer can build the application layer without first creating a model-training pipeline. The cited Howdy and Indeed figures show a 27% to 50% estimated cost difference between the US AI developer benchmark and Howdy's senior-level nearshore AI engineer pricing.

Examples:

• Seed-stage SaaS adding AI chat to their product
• Series A company automating customer support responses
• Startup building an AI writing assistant using an existing model API
• Team creating internal knowledge base search with embeddings

When to hire an ML engineer

Hire an ML engineer if your company needs to:

• Build proprietary models. Your competitive advantage depends on models trained on your unique data. Third-party APIs can't replicate your specific use case.
• Scale personalization. You have millions of users and need recommendation systems, content ranking, or behavior prediction models.
• Build under strict data controls. Healthcare, finance, and legal companies may restrict which data can go to third-party APIs. An ML engineer can support self-hosted or private model deployments when policy and technical requirements call for them.
• Optimize for specific constraints. You need models that run on edge devices, meet latency requirements under 50ms, or minimize compute costs at scale.

Examples:

• E-commerce company building product recommendation engine
• Fintech creating real-time fraud detection models
• Healthcare startup training diagnostic models on medical imaging
• Logistics company optimizing delivery route predictions

Can you hire both?

Yes. Hire both when one engineer must own the product integration while another owns model training and ML infrastructure. One possible progression follows.

Pre-seed through Series A

Hire one or two AI engineers when the immediate goal is to launch LLM-powered features and test demand.

Series A through Series B

Add ML engineers when proprietary data supports a defined modeling objective. AI engineers can own product integration, while ML engineers own training and model operations.

Series B and later

Add specialists as product usage and model operations require them. Company stage alone does not determine the appropriate team size.

Illustrative structure for a 50-person engineering group

• 3 AI engineers building product features
• 2 ML engineers training and deploying models
• 1 ML platform engineer managing infrastructure
• Backend, frontend, and data engineers supporting both

Frequently asked questions

Is an AI engineer higher than an ML engineer?

No. AI engineer and ML engineer are parallel roles, not hierarchical. Seniority depends on experience level (junior, mid, senior, staff), not title. A company can employ senior or staff-level engineers in either discipline. Compensation and decision-making authority depend on the employer, level, and role scope.

Can an ML engineer do AI engineering work?

Some ML engineers can integrate LLM APIs and build AI features, especially when they also have production software engineering experience. An AI engineer can move into ML engineering after developing the required statistics, model-training, data-pipeline, and model-operations skills. A job title alone does not establish whether someone can perform the other role.

Which role is more in demand?

The article's cited sources do not establish which role has greater overall demand. AI engineering openings tend to emphasize application integration with models from providers such as OpenAI and Anthropic, while ML engineering openings tend to emphasize training pipelines, proprietary data, and model operations. Compare current job-posting data in the location and industry where you plan to hire.

What's the difference between AI engineer, ML engineer, and data scientist?

Data scientist: Analyzes data, builds dashboards, creates statistical models, generates insights. Works earlier in the pipeline before product decisions.

ML engineer: Builds and deploys production machine learning systems. Focuses on model training, pipelines, and infrastructure.

AI engineer: Integrates existing AI models into product features. Focuses on application development and user-facing functionality.

A company may use all three roles. Data scientists can analyze opportunities, ML engineers can build production models, and AI engineers can integrate model capabilities into products.

Do I need a machine learning background to be an AI engineer?

An AI engineer does not always need prior model-training experience, but the role requires enough machine learning knowledge to evaluate outputs, choose integration patterns, and monitor model behavior. ML engineering roles more often require statistics, model evaluation, data pipelines, and training experience. Requirements vary with the product and model complexity.

How Howdy helps

Howdy is a nearshore hiring platform that connects US tech companies with AI and ML engineers in Latin America.

Howdy's process

1. Howdy helps you determine whether the work calls for an AI engineer, an ML engineer, or both.
2. Howdy evaluates candidates through technical interviews and reviews of past projects.
3. Howdy presents qualified candidates for your consideration.
4. You interview and select the engineer who fits the role.
5. Howdy handles payroll, benefits, and employment compliance.

Howdy's AI and ML hiring service

• Howdy reports a 98% developer retention rate. Add an inline link to Howdy's published methodology and reporting period before comparing it with an industry average.
• Howdy employs 31 recruiters with psychology backgrounds to support candidate assessment.
• Howdy operates physical "Howdy Houses" in more than 10 Latin American cities for in-person collaboration.
• Howdy's all-inclusive pricing covers payroll, benefits, and taxes.
• Add an inline link to Howdy's published fulfillment data before claiming that Howdy presents two or three qualified candidates within 48 hours.

Conclusion

Hire an AI engineer if you need to ship features fast using existing models. Hire an ML engineer if you're building proprietary models on your own data.

Start with the role that owns the first deliverable. Add the other role when product integration and proprietary model development become separate responsibilities.

Next steps

• Compare AI and ML engineer profiles
• See salary benchmarks and hiring timelines for your stage
• Talk to Howdy about your specific hiring needs

Book a demo with Howdy if you want help assessing or sourcing an AI engineer, an ML engineer, or both.