What Does an AI Engineer Do?
An AI engineer builds and maintains the software that puts machine learning models and large language models to work inside a product or a business process. The role sits between data science and software engineering. AI engineers take models that already exist, whether from an internal data science team or from a vendor such as OpenAI, Anthropic, or Google, and turn them into working features with APIs, data pipelines, evaluation, and monitoring. Most work for software companies, financial services firms, healthcare organizations, and logistics companies, and most employers require a bachelor’s degree in computer science or a related field plus 3 or more years of production software experience.
The title is often confused with two similar roles. A machine learning engineer trains and tunes models. A data scientist analyzes data and builds the first version of a model to answer a business question. An AI engineer is the person who ships the result: connecting a model to company data, writing the prompts and retrieval logic, testing output quality, controlling cost and latency, and keeping the feature working after launch.
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Hiring an AI Engineer: What to Expect
Candidates who hold the AI engineer title rarely come from research labs. Most are experienced software engineers who moved into LLM work from backend, data, or full-stack roles. Screen for a production AI feature the candidate shipped and still supports, for how they measured whether the model’s output was good enough, and for how they handled cost, latency, and sensitive data. A portfolio of notebooks is a weaker signal than one feature with real users.
The widest gap we see in AI engineer job descriptions is scope. Employers list model training, MLOps, data engineering, and application development in a single posting, and the people who can do all four are already employed. Decide which two of those your opening needs and write the description around those two.
A posting for this role should name the models and platforms the team uses, such as OpenAI, Anthropic, or Google models through their APIs or Amazon Bedrock, and say whether the work centers on RAG, agent workflows, evaluation, or fine-tuning. A requirement for deep learning research experience screens out many engineers who build and ship LLM features well, so include it only when the job involves model research.
On IT searches, 4 Corner Resources submits qualified candidates in an average of 3.7 days, clients interview an average of 1.42 candidates per hire, and we submit 1.86 candidates per hire.
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AI Engineer Core Responsibilities
- Design, build, and deploy AI-powered features such as chat assistants, document processing, search, classification, and recommendation systems
- Integrate large language models and other pretrained models into applications through vendor APIs or self-hosted deployments
- Build retrieval-augmented generation (RAG) pipelines, including document ingestion, chunking, embedding, and vector search
- Write, test, and version prompts, and build evaluation sets that measure output quality, accuracy, and safety before and after release
- Develop agent workflows that call tools, APIs, and internal systems to complete multi-step tasks
- Connect models to internal tools and data through function calling or the Model Context Protocol (MCP)
- Monitor model performance, cost, latency, and failure modes in production and fix regressions
- Work with data engineers to source, clean, and govern the data that models use
- Implement guardrails for sensitive data, prompt injection, and inappropriate output, and document how the system behaves
- Fine-tune or adapt models when prompting and retrieval are not enough, and document the tradeoffs
- Collaborate with product managers, designers, and subject matter experts to define what the feature should do and how success will be measured
- Compare new model releases and pricing against current evaluation results, and recommend when to switch vendors or approaches
- Review and test code from AI coding assistants such as GitHub Copilot, Cursor, or Claude Code before merging it
Required Skills and Qualifications
Hard skills
- Strong Python, plus experience with at least one application language such as TypeScript, Java, or Go
- Hands-on experience with LLM APIs (OpenAI, Anthropic, Google, or open-weight models such as Llama served through Hugging Face, vLLM, or Amazon Bedrock)
- RAG implementation experience, including embeddings and vector databases such as pgvector, Pinecone, Weaviate, or Elasticsearch
- Familiarity with orchestration and evaluation tooling such as LangChain, LangGraph, LlamaIndex, Ragas, or custom equivalents
- Experience with tool calling, structured outputs, and the Model Context Protocol (MCP)
- Familiarity with LLM tracing and observability tools such as LangSmith, Langfuse, or Arize Phoenix
- Software engineering fundamentals: version control, testing, CI/CD, containerization with Docker, and REST or gRPC API design
- Cloud experience on AWS, Azure, or Google Cloud, including managed AI services
- Working knowledge of machine learning concepts (model types, overfitting, evaluation metrics) sufficient to make good tradeoffs
- SQL and basic data pipeline skills
Soft skills
- Judgment about when an AI approach is the wrong tool for a problem
- Clear written communication, since prompts, evaluation criteria, and system behavior all need documentation
- Comfort working with ambiguous requirements and iterating quickly on feedback
- Ability to explain model limitations and risks to product managers, executives, and other nontechnical colleagues
- Attention to cost, since model usage is a line item that scales with traffic
Education
- Bachelor’s degree in computer science, software engineering, mathematics, or a related field
- Many employers accept equivalent production experience in place of a degree
Certifications
- None required. Cloud AI certifications such as AWS Certified Machine Learning Engineer Associate, Microsoft Azure AI Engineer Associate, or Google Professional Machine Learning Engineer are useful signals but rarely decisive
Preferred Qualifications
- A shipped AI feature with measurable results (adoption, accuracy, time saved, or cost reduced)
- Experience fine-tuning open-weight models or training small task-specific models
- Background in the employer’s domain, such as healthcare, financial services, legal, or logistics
- Experience with model security topics: prompt injection, data leakage, red-teaming, and the OWASP Top 10 for LLM Applications
- Contributions to open-source AI tooling or published technical writing
- Experience with speech, vision, or multimodal models in addition to text
- Experience with AI governance requirements such as the NIST AI RMF, ISO/IEC 42001, or the EU AI Act
How Much Does an AI Engineer Make?
Typical national range: $102,500 to $212,540, with a national average of $140,652. Salary data reviewed October 2026 by 4 Corner Resources.
AI engineer salaries vary by experience, industry, organization size, and geography. Click below to explore salaries by local market.
The average national salary for an AI Engineer is:
$140,652
Sample Job Description Templates for AI Engineers
AI Engineer (Mid-Level)
Position Overview
We are hiring an AI Engineer to build and support the AI features in our product. You will integrate large language models with our application and data, build retrieval and evaluation pipelines, and own the quality, cost, and reliability of what ships. This is a hands-on engineering role on a small team with direct access to product and customers.
Responsibilities
- Build and maintain LLM-powered features from prototype through production
- Design and implement RAG pipelines over company documents and structured data
- Build tool-calling and agent workflows that connect models to internal APIs
- Create evaluation sets and automated tests for model output quality
- Monitor usage, cost, and latency, and tune prompts, models, and caching to meet targets
- Implement guardrails for sensitive data, prompt injection, and inappropriate output
- Document system behavior and known limitations for the rest of the team
- Review and test code suggested by AI coding assistants such as GitHub Copilot or Cursor before merging
Requirements
Hard skills
- 3 or more years of professional software engineering experience, with at least 1 year building LLM-based features
- Strong Python and experience with a web backend framework such as FastAPI or Django
- Experience with at least one major LLM API and one vector database
- Familiarity with cloud deployment and containerization
Soft skills
- Pragmatic about tradeoffs between quality, speed, and cost
- Writes clearly and documents as part of the work
- Collaborates well with product and nontechnical partners
Education
- Bachelor’s degree in computer science or a related field, or equivalent experience
Senior AI Engineer
Position Overview
We are seeking a Senior AI Engineer to lead the design of our AI platform and mentor a growing team. You will set the technical direction for how we build, evaluate, and operate AI features, make build-versus-buy decisions on models and tooling, and stay hands-on in the code. The role reports to the Director of Engineering.
Responsibilities
- Own the architecture for AI features, including model selection, retrieval, orchestration, and evaluation
- Establish engineering standards for prompt management, testing, observability, and rollout
- Set standards for agent permissions, tool access, and human approval steps
- Lead build-versus-buy evaluations of models, vendors, and tooling, including cost modeling
- Review designs and code from other engineers and mentor them on AI engineering practice
- Partner with security and compliance on data handling, access control, and audit requirements
- Represent the engineering view in product planning and roadmap discussions
Requirements
Hard skills
- 6 or more years of software engineering experience, with 2 or more years leading LLM or ML systems in production
- Deep experience with RAG, agent frameworks, fine-tuning, and evaluation methodology
- Experience with the Model Context Protocol (MCP) and multi-agent orchestration
- Experience operating AI systems at scale, including cost and latency optimization
- Strong cloud and infrastructure skills on AWS, Azure, or Google Cloud
Soft skills
- Able to make and explain decisions under uncertainty
- Mentors without taking over the work
- Communicates risk and tradeoffs plainly to leadership
Education
- Bachelor’s degree in computer science or a related field required; master’s degree preferred
Preferred qualifications
- Experience with regulated data (HIPAA, SOC 2, PCI, or similar)
- Prior experience as a tech lead or staff engineer
- Experience with AI governance frameworks such as the NIST AI RMF or ISO/IEC 42001
Generative AI Engineer (LLM Applications)
Position Overview
We are hiring a Generative AI Engineer to build conversational and content-generation features on top of large language models. You will design prompts and workflows, connect models to our data and tools, measure output quality, and improve the experience based on real usage. The role suits an engineer who enjoys rapid iteration and close work with users.
Responsibilities
- Design and ship chat, summarization, drafting, and search features built on LLMs
- Write and maintain prompt libraries with version control and test coverage
- Build agent workflows that call internal tools and APIs through function calling or MCP, with human approval for actions that change data
- Conduct human and automated evaluations and turn the findings into product improvements
- Instrument features to capture usage and feedback
- Work with design and support teams to handle edge cases and failure states, such as low-confidence answers and model timeouts
Requirements
Hard skills
- 2 or more years of software engineering experience and demonstrated LLM application work
- Strong Python or TypeScript
- Experience with LLM APIs, embeddings, and a vector store
- Familiarity with evaluation frameworks and observability tools for LLM applications, such as Ragas, Langfuse, or LangSmith
Soft skills
- Curiosity about how people use the product day to day
- Comfortable shipping, measuring, and revising in short cycles
- Careful with user data and candid about model limitations
Education
- Bachelor’s degree in a technical field, or equivalent experience
AI Engineer, Enterprise Applications (Non-Software Employer)
Position Overview
Our organization is adding an AI Engineer to our internal technology team to build AI tools for operations, customer service, and back-office functions. You will work with business leaders to identify where AI can reduce manual work, build and deploy solutions on our approved cloud platform, and make sure every deployment meets our data governance and security standards. This is a new role with high visibility and room to shape how we use AI.
Responsibilities
- Partner with department leaders to identify, scope, and prioritize AI use cases
- Build and deploy AI solutions on the company’s cloud platform (Azure, AWS, or Google Cloud) using vendor APIs and managed services such as Amazon Bedrock, Google Vertex AI, or Azure OpenAI
- Connect models to internal systems such as the CRM, ERP, ticketing, and document repositories
- Build agents in Microsoft Copilot Studio or similar low-code tools for department workflows, and check data permissions before rollout
- Design evaluation and acceptance criteria with business owners before launch
- Implement data governance, access controls, and logging in line with IT security policy
- Train internal users and document how each solution works and where it should not be used
Requirements
Hard skills
- 4 or more years of software or systems engineering experience, with recent LLM application work
- Experience integrating with enterprise systems and APIs
- Working knowledge of enterprise security and compliance requirements
- Python plus familiarity with low-code and workflow tools used by the business, such as Power Automate, Copilot Studio, or Zapier
Soft skills
- Translates between technical and business language
- Patient with change management and training
- Prioritizes reliability and governance over novelty
Education
- Bachelor’s degree in computer science, information systems, or a related field
Certifications
- Microsoft Azure AI Engineer Associate, AWS Certified Machine Learning Engineer Associate, or Google Professional Machine Learning Engineer preferred