AI ENGINEER JOB DESCRIPTION

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An AI Engineer is responsible for building and improving AI systems that solve practical business problems.

This can include developing AI applications, integrating models into products, building data and model pipelines, working with LLMs, creating automation workflows, improving model performance and supporting AI systems in production.

AI Engineers often work closely with data scientists, machine learning engineers, data engineers, software engineers, product teams, security specialists and business stakeholders.



The exact responsibilities can vary depending on the organisation. Some AI Engineers focus on LLMs and GenAI applications. Others work on machine learning models, AI automation, AI platforms, model deployment, conversational AI, computer vision, NLP or decision intelligence.

An AI Engineer may be responsible for:

• designing, building and improving AI systems and applications
• integrating AI models into products, platforms and workflows
• working with LLMs, GenAI tools, APIs and AI frameworks
• developing prompts, retrieval workflows and model evaluation processes
• building and maintaining data and model pipelines
• supporting machine learning model development and deployment
• creating AI-powered automation workflows
• developing APIs and services that connect AI capability to business systems
• monitoring AI system performance, reliability and output quality
• testing, evaluating and improving AI model responses
• supporting MLOps, LLMOps or model lifecycle management
• working with cloud platforms and AI services
• collaborating with data science, engineering, product and business teams
• documenting technical decisions, risks, assumptions and model behaviour
• supporting responsible AI, governance, privacy and security requirements 

A strong AI Engineer usually combines software engineering, data understanding, machine learning knowledge and practical AI implementation experience.

Common AI Engineer skills include:

Python

SQL

APIs

software engineering

machine learning

LLMs and GenAI

prompt engineering

retrieval augmented generation

model evaluation

data pipelines

cloud platforms

MLOps or LLMOps

Docker and Kubernetes

CI/CD

vector databases

AI frameworks and orchestration tools

testing and experimentation

model monitoring

responsible AI understanding

privacy and security awareness

communication

problem-solving

product thinking



The exact skill set will depend on the role. An AI Engineer building GenAI applications may need stronger LLM, RAG, API and product integration experience. An AI Engineer working closer to machine learning may need stronger model development, MLOps, cloud and data pipeline experience.

The below example pulls in the responsibilities and skills across a broad spectrum, as a guide, so please tailor it to your role and business.  

We are looking for an AI Engineer to design, build and improve AI systems and applications.

The successful candidate will work with data, engineering, product and business teams to develop practical AI solutions that support real business needs.

You will be responsible for building AI-enabled applications, integrating models and AI services, developing data and model pipelines, evaluating outputs, improving performance and helping AI systems operate reliably and safely in production.

Key Responsibilities

Design, build and improve AI systems and applications.

Integrate AI models, APIs and services into products, platforms and workflows.

Work with LLMs, GenAI tools and AI frameworks.

Develop prompts, retrieval workflows and model evaluation processes.

Build and maintain data and model pipelines.

Support machine learning model development and deployment.

Create AI-powered automation workflows.

Develop APIs and services that connect AI capability to business systems.

Monitor AI system performance, reliability and output quality.

Test, evaluate and improve AI model responses.

Support MLOps, LLMOps or model lifecycle management.

Work with cloud platforms and AI services.

Collaborate with data science, engineering, product and business teams.

Document technical decisions, assumptions, risks and model behaviour.

Support responsible AI, privacy, security and governance requirements.

Required Skills and Experience

Experience in AI engineering, machine learning engineering, software engineering, data science or applied AI.

Strong Python skills.

SQL or data querying experience.

Experience building AI applications, ML systems or AI-powered workflows.

Experience working with LLMs, GenAI tools, machine learning models or AI APIs.

Understanding of APIs, software engineering and system integration.

Experience with cloud platforms such as AWS, Azure or Google Cloud.

Experience with data pipelines, databases or data platforms.

Understanding of model evaluation, testing and monitoring.

Strong problem-solving and communication skills.

Ability to work with technical and non-technical stakeholders.

Desirable Skills

Experience with OpenAI, Anthropic, Gemini, Hugging Face or open-source models.

Experience with LangChain, LlamaIndex, Semantic Kernel or similar tools.

Experience with vector databases and search tools.

Experience with RAG, prompt engineering or LLM evaluation.

Experience with MLOps or LLMOps tools such as MLflow, Kubeflow or similar platforms.

Experience with Docker, Kubernetes, CI/CD or infrastructure tooling.

Experience with Databricks, Snowflake, BigQuery or similar data platforms.

Experience with Azure AI, AWS Bedrock, Amazon SageMaker or Google Vertex AI.

Understanding of responsible AI, explainability, privacy, security and governance.

Experience taking AI systems from prototype to production.

AI Engineer salaries and day rates can vary significantly depending on location, seniority, technical depth, industry, working model and whether the role is permanent or contract.

Employers should benchmark salary before going to market, especially for roles requiring LLMs, GenAI, MLOps, LLMOps, cloud platforms, AI architecture, model deployment or production AI experience.

Candidates will often consider the full package, not just base salary. Remote flexibility, bonus, equity, pension, learning opportunities, technical ownership, product impact, ethical AI practice and career progression can all influence whether a role is attractive.

KDR can support employers with salary benchmarking and market insight before a search begins.

Hiring an AI Engineer starts with understanding what kind of AI capability the organisation needs to build.

Before going to market, employers should be clear on:

• whether the role is focused on LLMs, GenAI, machine learning, automation, AI products, platform engineering or production systems
• what AI systems, applications or workflows the person will build or improve
• whether the work is experimental, production-focused or a blend of both
• which tools and platforms are essential
• what data environment the person will work with
• what level of software engineering experience is required
• what level of ownership the person will have
• what salary or day rate range is realistic
• how the hiring process will assess practical AI engineering ability

KDR helps organisations hire AI Engineers across permanent, contract and senior roles. We can support role definition, candidate search, shortlist creation, interview coordination, salary advice and offer management.

AI Engineers can move in several directions as their experience grows.

Some progress into Senior AI Engineer, Lead AI Engineer, Principal AI Engineer, AI Architect, Head of AI Engineering or Director of AI roles. Others move towards machine learning engineering, MLOps, AI platform engineering, software architecture, data science or AI product leadership.

A typical career path may include:

• Software Engineer, Data Scientist, Machine Learning Engineer or Data Engineer
• AI Engineer
• Senior AI Engineer
• Lead AI Engineer
• Principal AI Engineer
• AI Architect
• Head of AI Engineering
• Head of AI
• Director of AI
• Chief AI Officer



Career progression often depends on technical depth, production AI experience, software engineering quality, stakeholder influence, architecture ownership and the ability to connect AI delivery to business outcomes.