Generative AI Engineer

Generative Ai Engineer83zero Limited

2 days ago

Role details

Contract type

Permanent contract

Employment type

Part-time (≤ 32 hours)

Working hours

Regular working hours

Languages

English

Compensation

£ 89K

Job location

Tech stack

JavaScript

Artificial Intelligence

Amazon Web Services (AWS)

Azure

Cloud Computing

Databases

Continuous Integration

Github

Python

PostgreSQL

MongoDB

NoSQL

Next.js

SQL Databases

TypeScript

React

Large Language Models

Infrastructure as Code (IaC)

Backend

Gitlab

GIT

FastAPI

Containerization

WebPack

Kubernetes

Front End Software Development

Terraform

Software Version Control

Docker

Jenkins

Microservices

Job description

We're looking for a GenAI Full Stack Engineer who is passionate about solving real-world challenges through technology. You'll work closely with senior stakeholders both internally and within key clients to create GenAI strategies that translate business issues into relevant technical solutions and competitive propositions that are scalable, secure, and sustainable.

Your Role:

Collaborate with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents.
Scale existing POCs to production-ready solutions for customer use.
Design and develop Full Stack applications for both GenAI and non-GenAI projects.
Support the development of infrastructure to enable robust end-to-end solutions.
Grow your skills by exploring and experimenting with emerging technologies, including GenAI.

Requirements

Backend Development: Experience in building microservices using either Python with FastAPI, or TypeScript with Express or equivalent
Frontend Development: Experience in building React applications using TypeScript, leveraging frameworks such as Next.js or Vite.
GenAI Frameworks: Familiarity with tools like LangChain, LangGraph, CrewAI, or Vercel AI (in Python or JavaScript).
LLM experience: Practical experience with Large Language Models (LLMs) such as OpenAI, Anthropic, Mistral, or Llama, and tools like Ollama.
Cloud Platforms: Proficiency in AWS or Azure for hosting and deployment.
Containerization and Orchestration: Hands-on experience with Docker, Kubernetes, and Docker Compose.
Infrastructure as Code (IaC): Exposure to tools like Terraform.
Monitoring Tools: Familiarity with LLM monitoring tools like Langsmith, Langfuse, or similar.
CI/CD: Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins.
Vector Databases: Experience with and (but not limited to) ChromaDB, Pinecone, PGVector, MongoDB , Qdrant etc.
NoSQL: Familiarity with NoSQL databases (e.g., MongoDB preferred).
SQL: Experience working with SQL databases like PostgreSQL.
Proficient in Git and version control platforms like GitHub, GitLab, etc.