GenAI Application Developer

Randstad UK
Belfast, United Kingdom
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
£ 75K

Job location

Belfast, United Kingdom

Tech stack

Artificial Intelligence
Azure
Cloud Computing
Continuous Integration
Persistent Data Structure
DevOps
Python
MongoDB
NumPy
Openshift
Unstructured Data
Delivery Pipeline
Generative AI
Gitlab
FastAPI
Pandas
Kubernetes
Data Pipelines
Jenkins
Microservices

Job description

  • Develop and engineer production-grade, autonomous systems using Python
  • Build high-throughput microservices and complex data pipelines
  • Design and implement Retrieval-Augmented Generation (RAG) pipelines
  • Create and manage Agentic frameworks using relevant tools
  • Design data persistence layers to manage unstructured data efficiently
  • Deploy GenAI models into production environments
  • Establish and maintain CI/CD pipelines for seamless integration and delivery
  • Assess the performance and safety of GenAI features for compliance with rigorous standards

Technologies:

  • AI
  • Azure
  • CI/CD
  • DevOps
  • FastAPI
  • GitLab
  • Jenkins
  • Kubernetes
  • OpenShift
  • Python
  • microservices
  • numpy
  • pandas
  • Cloud
  • Architect

Requirements

  • 6-10 years of professional experience in Python development
  • Expertise in building high-throughput microservices and complex data pipelines using FastAPI, Pandas, and NumPy
  • Deep hands-on experience building Retrieval-Augmented Generation (RAG) pipelines and Agentic frameworks
  • Proficiency with LangChain or LlamaIndex for creating AI that executes multi-step tasks
  • Comfortable designing data persistence layers using PG Vector, Pinecone, Milvus, or Mongo Atlas
  • Experience deploying GenAI models into production using Kubernetes or OpenShift
  • Familiarity with establishing robust CI/CD pipelines via Jenkins, GitLab, or Azure DevOps
  • Working knowledge of Guardrails to assess performance and safety of GenAI features

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