AI-related engineering roles are becoming harder to distinguish.
Open a few job descriptions, and you'll find similar requirements: Python, APIs, cloud infrastructure, machine learning frameworks, LLMs, and production experience.
But an AI Engineer, an ML Engineer, and a Forward-Deployed Engineer don't necessarily solve the same problems.
The difference becomes clearer when you look at what each role is expected to deliver.
1. AI Engineer: Turning AI Capabilities Into Product Features
An AI Engineer typically focuses on integrating AI capabilities into software applications.
Imagine a SaaS company wants to introduce an AI assistant that can answer questions using internal documentation and customer account data.
The team might use an existing language model rather than train one from scratch.
But there's still considerable engineering work involved:
- Connecting the model to application APIs and data sources
- Implementing retrieval-augmented generation (RAG), when appropriate
- Managing prompts, context, and conversation state
- Validating model outputs
- Handling authentication and access permissions
- Monitoring latency, reliability, and API costs
The model is only one component of the feature.
The AI Engineer's responsibility is to make it work within the product's existing architecture.
Typical skills: Python, JavaScript or TypeScript, backend development, REST APIs, LLM APIs, RAG, databases, cloud services, and AI evaluation.
Example deliverable: An AI-powered support assistant integrated into an existing SaaS platform.
2. ML Engineer: Building and Operating Machine Learning Systems
ML Engineers focus more directly on the machine learning lifecycle.
Their work often involves preparing datasets, training models, evaluating performance, building pipelines, and deploying models into production.
Consider a fintech company developing a fraud detection system.
Using a general-purpose LLM may not be the most suitable approach. The company might need a model trained on transaction patterns, historical fraud cases, and domain-specific signals.
An ML Engineer could be responsible for:
- Preparing training and validation datasets
- Developing feature engineering pipelines
- Training and evaluating candidate models
- Optimising inference performance
- Deploying models through production services
- Monitoring model quality and data drift
- Managing retraining workflows
Unlike many application-focused AI engineering tasks, this work may require deeper knowledge of statistics, machine learning algorithms, and model evaluation.
Typical skills: Python, SQL, PyTorch, TensorFlow, scikit-learn, data pipelines, experiment tracking, model serving, and MLOps.
Example deliverable: A production fraud detection model supported by automated training, deployment, and monitoring pipelines.
3. Forward-Deployed Engineer: Making Technology Work in Customer Environments
Forward-Deployed Engineers (FDEs) operate at the intersection of software engineering, customer collaboration, and implementation.
Their responsibilities depend heavily on the company and product.
Imagine an enterprise AI platform that needs to be deployed for several customers.
Each customer has different databases, internal APIs, security requirements, and business workflows.
The underlying platform may be the same, but the implementation challenges are not.
An FDE might:
- Work directly with customer engineering teams
- Understand technical requirements and operational constraints
- Integrate the platform with existing systems
- Build customer-specific extensions or workflows
- Troubleshoot deployment issues
- Adapt implementations based on real-world feedback
- Share recurring customer requirements with the core product team
An FDE is not simply a consultant who provides recommendations.
The role typically involves hands-on engineering and responsibility for getting a solution working in the customer's environment.
Typical skills: Software engineering, APIs, system integration, cloud infrastructure, debugging, solution architecture, and technical communication.
Example deliverable: A customer-specific deployment of an AI platform integrated with enterprise systems.
4. Comparing the Three Roles
| Area | AI Engineer | ML Engineer | Forward-Deployed Engineer |
|---|---|---|---|
| Primary focus | AI-powered product features | ML models and pipelines | Customer-specific implementation |
| Main responsibility | Integrate AI into applications | Develop and operate ML systems | Adapt and deploy solutions |
| Typical environment | Product engineering team | ML or data engineering team | Customer-facing engineering |
| Common challenges | Integration, reliability, latency | Model quality, data drift, MLOps | Legacy systems, customer requirements |
| Typical output | AI-enabled application feature | Production ML system | Working customer deployment |
These are common distinctions rather than strict boundaries.
An AI Engineer may work on model evaluation. An ML Engineer may build application APIs. An FDE may implement complex AI workflows.
The exact responsibilities depend on the organisation.
5. How to Decide Which Engineer You Need
Start with the technical problem rather than the job title.
Scenario A: You're adding an LLM feature to an existing product
You want to introduce semantic search, document summarisation, or an AI assistant.
Your main challenges involve connecting an existing model to application data, managing outputs, and maintaining reliability.
Likely fit: AI Engineer.
Scenario B: You're developing a custom prediction model
Your product depends on domain-specific data, model training, evaluation, and continuous improvement.
Likely fit: ML Engineer.
Scenario C: Your platform requires customer-specific deployments
You already have a product, but each customer needs integration with different systems, data sources, and infrastructure.
Likely fit: Forward-Deployed Engineer.
Scenario D: Your existing team lacks one specific area of expertise
You have backend and frontend developers, but nobody has production experience with LLM integration or ML pipelines.
You may not need to build an entirely new department.
Adding an experienced specialist through staff augmentation can help address the gap while keeping engineering ownership within your existing team.
The engagement model matters, but defining the technical responsibilities comes first.
6. What to Evaluate During Technical Interviews
The interview process should reflect the actual work.
For an AI Engineer, discuss system design, model integration, retrieval strategies, output validation, and failure handling.
For an ML Engineer, explore model evaluation, dataset quality, training pipelines, deployment, and monitoring.
For a Forward-Deployed Engineer, focus on integration scenarios, debugging unfamiliar systems, technical trade-offs, and communication with customer stakeholders.
A candidate's familiarity with popular AI tools is useful, but production engineering experience is often more revealing.
Ask what they built, how it was deployed, what went wrong, and how they handled it.
Final Thoughts
AI Engineer, ML Engineer, and Forward-Deployed Engineer are overlapping but distinct roles.
The right choice depends on whether your main challenge is integrating AI into a product, developing machine learning systems, or implementing solutions in customer environments.
Before hiring, define the expected deliverables, technical constraints, and expertise already available within your team.
For a broader comparison of responsibilities, skills, and hiring considerations, read the guide from UNL Solutions:
AI Engineer vs ML Engineer vs Forward-Deployed Engineer: Who Do You Need?
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