AI Engineer vs ML Engineer: Roles Compared

Table of contents

1. AI engineer vs ML engineer: Quick comparison
2. What is an AI engineer?
3. What is an ML engineer?
4. Key differences between AI engineers and ML engineers
5. What to hire if you have little or no data
6. Salaries and costs: US vs nearshore Latin America
7. When to hire an AI engineer
8. When to hire an ML engineer
9. Can you hire both?
10. Frequently asked questions
11. How Howdy helps
12. Conclusion

AI engineers ship features using existing models like GPT-4 and Claude. They integrate LLM APIs, build chatbots, and automate workflows. ML engineers build, adapt, and deploy models, often training or fine-tuning on proprietary data. They create recommendation engines, fraud detection systems, and train models on company-specific datasets.

Many teams start with AI engineers to ship LLM-powered features quickly. Companies add ML engineers once they have proprietary data, clear metrics to improve, and a need for models that outperform generic baselines.

Note: In Latin America (LatAm), AI vs ML comp often overlaps; pricing is driven more by seniority and location than title.

Not sure which role you need first? Book a demo with Howdy. We’ll recommend the right hire — AI, ML, or data engineering — based on your data maturity, security constraints, and roadmap.

What is an AI engineer?

An AI engineer applies existing AI models to product problems. The role focuses on execution and delivery, not 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 tech stack:

• 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 don’t train foundation models. Instead, they may fine-tune smaller models or manage RAG/evals depending on the team.

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 tech stack:

• 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 create proprietary models that competitors can't easily replicate.

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.

When companies hire each role

AI engineers are usually the first hire for shipping LLM features; ML engineers come next when proprietary data and measurable lift matter.

What to hire if you have 0 data (or your data is messy)

Many midmarket teams want “AI features,” but don’t yet have clean, centralized datasets ready for model training. In that case, the right first hire depends on what you do have: workflows, documents, or event data.

If you have strong internal documentation (but limited structured data)

Hire an AI engineer first. They can ship value quickly using RAG (retrieval-augmented generation) over your existing knowledge base (docs, tickets, wikis, PDFs) without waiting for a full data 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 you have data scattered across systems (CRM, product, support) with no 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 “done” looks like:

• 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 you have sensitive/regulatory constraints that limit third-party APIs

Hire an AI engineer with security/compliance experience first (and plan to add ML later if needed). They can implement guardrails, redaction, vendor reviews, and private deployments — then build toward fine-tuning or custom models once governance is in place.

Clarifier: If the goal is LLM features under compliance constraints, start with an AI engineer who can implement governance and private deployments. If the goal is predictive modeling (fraud/risk/forecasting) on restricted data, you’ll likely need an ML engineer (and strong data engineering) earlier.

If you have high-volume product/event data and a clear metric to improve

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 wins:

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

Rule of thumb: If your fastest path to ROI is workflow automation and knowledge access, start with an AI engineer. If your advantage depends on predictive performance on proprietary event data, start with an ML engineer.

CTA: Not sure which path fits your data maturity? Book a demo with Howdy. We’ll recommend the right first hire and team shape for your use case. Typical timeline: 2–3 candidates in 48 hours.

Salaries and costs

United States (2025)

According to Indeed salary data (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)

Based on Indeed averages, ML engineer base pay is ~23% higher than AI engineer base pay. In practice, the premium is driven by role scope (training pipelines, data infrastructure, model ownership) and a smaller pool of candidates with production ML experience.

Total US employer cost (including benefits and taxes):

• AI engineer: $149K salary + $62K benefits/taxes = $211K annually
• ML engineer: $183K salary + $76K benefits/taxes = $259K annually

Nearshore Latin America (2025)

Howdy all-inclusive pricing (salary + benefits + taxes + platform fee):

AI engineer:

• Colombia, Brazil: $70K–$76K
• Chile, Mexico: $80K
• Argentina, Uruguay: $90K–$92K
• Peru: $100K

ML engineer:

• Colombia, Brazil: $70K–$76K
• Chile, Mexico: $80K
• Argentina, Uruguay: $90K–$92K
• Peru: $100K

Methodology note: Ranges reflect typical Howdy all-inclusive pricing (salary, benefits, taxes, and platform fee). Final pricing varies by seniority, English level, and role calibration.

AI and ML engineers in Latin America command similar salaries. The role distinction matters less than experience level and location.

Cost savings comparison:

• US AI engineer: $211K vs Nearshore: $70K–$100K = 57–67% savings
• US ML engineer: $259K vs Nearshore: $70K–$100K = 61–73% savings

Nearshore engineers work in US-aligned time zones (UTC-3 to UTC-6) with cultural compatibility and English fluency.

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.
• Work within budget constraints. AI engineers cost less than ML engineers and deliver immediate ROI. Nearshore AI engineers offer additional 30–50% savings.

Examples:

• Seed-stage SaaS adding AI chat to their product
• Series A company automating customer support responses
• Startup building an AI writing assistant using GPT-4
• 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.
• Handle sensitive or regulated data. Healthcare, finance, and legal companies often can't send data to third-party APIs. On-premises models are required.
• 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, and many companies do. The typical progression:

Stage 1 (Pre-Seed to Series A): Hire 1-2 AI engineers to ship LLM-powered features and validate product direction.

Stage 2 (Series A to Series B): Add ML engineers once you have proprietary data worth modeling. AI engineers focus on product features; ML engineers build infrastructure.

Stage 3 (Series B+): Scale both teams. AI engineers own application-layer AI. ML engineers own the model layer, data pipelines, and training infrastructure.

Example team structure at Series B (50-person eng team):

• 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 senior AI engineer and senior ML engineer at the same company earn comparable salaries and have equal influence.

Can an ML engineer do AI engineering work?

Yes. ML engineers have the technical depth to integrate LLM APIs and build AI features. Many ML engineers transition into AI engineering roles when companies shift focus from custom models to faster LLM integration.

The reverse is harder. AI engineers without formal ML training struggle with model architecture decisions, training pipelines, and optimization at scale.

Which role is more in demand in 2025?

AI engineers. The explosion of LLM adoption created massive demand for engineers who can ship features quickly using OpenAI, Anthropic, and other foundation models.

ML engineer demand remains strong but concentrated in later-stage companies with proprietary data and existing infrastructure.

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.

Many teams have all three roles with clear handoffs: data scientists identify opportunities, ML engineers build models, AI engineers ship features.

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

No. AI engineers need strong software engineering skills, API integration experience, and product intuition. Understanding how models work helps but isn't required for day-to-day work.

ML engineers require deeper math foundations (linear algebra, calculus, statistics) and experience training models from scratch.

How Howdy helps

Howdy connects US tech companies with nearshore AI and ML engineers in Latin America.

Our process:

1. We help you determine whether you need an AI engineer, ML engineer, or both based on your product stage and data maturity
2. We vet engineers for real-world skills (not just credentials) through technical interviews and past project evaluation
3. We present 2-3 qualified candidates within 48 hours
4. You interview and select the engineer who fits your team
5. We handle payroll, benefits, compliance, and retention

Why companies choose Howdy for AI and ML hiring:

• 98% developer retention rate versus 30-50% industry average
• 31 recruiters with psychology backgrounds for culture-fit matching
• Physical "Howdy Houses" in 10+ Latin American cities for team collaboration
• All-inclusive pricing (no hidden fees for payroll, benefits, or taxes)
• Present 2-3 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.

Most companies start with AI engineers to validate product direction, then add ML engineers once scale and differentiation matter more than speed.

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 to get started.