AI Engineer

GCS; Dublin; Hybrid; EUR 130,000 - 170,000; Full time

Job summary

GCS is seeking an experienced Full‑Stack AI Engineer for a 12‑month hybrid contract. You will design GenAI and agentic AI applications, build with AWS Bedrock, and implement complex RAG pipelines and multi‑agent workflows.

You will deliver production‑grade solutions, support existing systems, and upskill teams through technical training and workshops. Strong communication and hands‑on development are essential for driving adoption across business units.

Qualifications

• 5+ years software engineering experience.
• 2+ years production AI/ML or GenAI experience.
• Strong AWS technology stack experience.
• Experience delivering production applications.

Responsibilities

• Design, develop and deliver GenAI and agentic AI applications.
• Build and integrate AWS Bedrock based solutions and multi‑agent workflows.
• Develop RAG pipelines with embeddings, vector stores and parsing.
• Integrate MCP servers into AI applications and workflows.
• Fine‑tune models for performance and efficiency.
• Develop AI solutions for document processing and enterprise apps.
• Build full‑stack apps using Python, FastAPI and React.
• Develop streaming UIs and real‑time chat interfaces.
• Design and integrate REST APIs, WebSockets and SSE.
• Work with PostgreSQL and DynamoDB databases.

Skills

GenAI experience

LLM concepts

Multi-agent workflows

MCP protocol

RAG architectures

Python

FastAPI

React

REST APIs

WebSockets

SSE

OAuth 2.0

JWT

PostgreSQL

DynamoDB

CI/CD

Infrastructure as Code

Tools

AWS Bedrock

PostgreSQL

DynamoDB

Python

FastAPI

React

REST APIs

WebSockets

SSE

OAuth 2.0

JWT

AWS CDK

Lambda

API Gateway

Step Functions

EventBridge

S3

Job description

We are seeking an experienced Full-Stack AI Engineer to join our team on a 12-month contract basis. This is a hybrid delivery role combining hands‑on development of GenAI-powered applications with a strong focus on training, technical enablement, and product adoption.

You will design and build production‑grade AI solutions, support and maintain existing applications, and help upskill development teams and business stakeholders through technical training, workshops, and knowledge‑sharing sessions.

The ideal candidate is technically strong, hands‑on, a confident communicator and passionate about helping teams adopt emerging AI technologies.

Key Responsibilities

• Design, develop, and deliver production‑grade GenAI and agentic AI applications.
• Build and integrate solutions using AWS Bedrock, foundation models, knowledge bases, model evaluations, guardrails, and multi‑agent workflows.
• Develop RAG pipelines, including embedding models, vector stores, document chunking, parsing, and retrieval strategies.
• Integrate MCP (Model Context Protocol) servers into AI applications and workflows.
• Fine‑tune and optimise models for performance, efficiency, and business use cases.
• Develop AI solutions for complex document processing projects.
• Build full‑stack applications using Python, FastAPI, and React.
• Develop streaming UIs, real‑time chat interfaces, and enterprise applications.
• Design and integrate REST APIs, WebSockets, and Server‑Sent Events (SSE).
• Work with both relational and NoSQL databases, including PostgreSQL and DynamoDB.
• Implement secure authentication and authorisation using AWS Cognito, IAM, OAuth 2.0, and JWT.
• Build and maintain AWS cloud infrastructure using services such as Lambda, API Gateway, Step Functions, EventBridge, S3, DynamoDB, CloudWatch, SQS, and Bedrock.
• Develop Infrastructure as Code using AWS CDK.
• Contribute to CI/CD pipelines, automated testing, and version‑controlled development practices.
• Support, maintain, troubleshoot, and enhance existing production applications.
• Deliver technical training and product enablement sessions to development teams and business stakeholders.
• Develop training materials, workshops, technical documentation, and reference guides.
• Coach teams and facilitate knowledge transfer to build sustainable internal capabilities.
• Document code, architecture, technical specifications, and implementation approaches.

Required Skills & Experience

GenAI & Agentic AI

• Strong hands‑on experience with Generative AI and LLM‑based applications.
• Production experience with AWS Bedrock.
• Experience with foundation models, knowledge bases, model evaluation, guardrails, and multi‑agent workflows.
• Experience integrating MCP (Model Context Protocol).
• Strong understanding of RAG architectures, embeddings, vector stores, chunking, parsing, and retrieval strategies.
• Experience fine‑tuning or optimising models for performance and efficiency.
• Experience applying LLMs to complex document processing use cases.

Full‑Stack Development

• Hands‑on experience with FastAPI.
• Experience with asynchronous programming and multi‑threaded applications.
• Experience building streaming UIs, real‑time chat applications, or enterprise web applications.
• Strong understanding of REST APIs, WebSockets, and Server‑Sent Events (SSE).
• Experience with SQL and NoSQL databases, particularly PostgreSQL and DynamoDB.
• Good understanding of version control, testing, and software engineering best practices.

AWS & Cloud

• Strong hands‑on experience across the AWS technology stack.
• Experience with:
• AWS Bedrock
• Lambda
• API Gateway
• Step Functions
• EventBridge
• S3
• DynamoDB
• CloudWatch
• SQS
• Cognito
• IAM
• Strong understanding of OAuth 2.0 and JWT token management.
• Experience with Infrastructure as Code, preferably AWS CDK.
• Experience building and maintaining CI/CD pipelines.

Training & Enablement

• Experience delivering technical training or product enablement sessions.
• Ability to explain complex technical concepts to both technical and non‑technical audiences.
• Experience creating workshops, training materials, technical documentation, and reference guides.
• Strong coaching, mentoring, and knowledge‑transfer skills.

Experience Required

• 5+ years of software engineering experience.
• 2+ years of production experience in AI/ML or GenAI development.
• Proven hands‑on experience with the AWS technology stack.
• Experience delivering and supporting production applications.
• Strong problem‑solving and troubleshooting skills.
• Ability to work independently and take ownership of deliverables.
• Excellent written and verbal communication skills.
• Strong documentation and technical writing skills.