Point your AI agent at freehire and let it find you a job. A CLI and an MCP server over the whole job API — no browser.
Build and optimize scalable data pipelines and cloud-native services using Python/Java, Spark, and Kubernetes to power GenAI applications and LLM workflows.
Build and deploy AI agents and predictive models to automate clinical research workflows, analyze trial data, and generate regulatory-compliant insights using Python, scikit-learn, TensorFlow, and LLM frameworks.
Build and deploy AI-powered full-stack apps using Python/Django, Angular, and Azure AI. Focus on LLMs, prompt engineering, and agent workflows with Docker and LangChain.
Build and run the backend for a new generative-AI hub serving a financial-services firm; stack includes Azure, .NET/Node.js, vector search, agent orchestration, and Azure OpenAI.
Develops an agentic AI framework (JARVIS) for pharma use cases, designing Python-based LLM workflows, Streamlit interfaces, and Kubernetes deployments to validate technical feasibility for a future pilot.
Build and deploy generative AI solutions in Python, designing backend architectures, RAG systems, and AI agents while implementing DevOps practices on GCP.
Lead the design and production deployment of scalable AI systems, including GenAI, agentic AI, and LLMOps, while guiding engineering teams and setting technical standards.
Build the backend for an Agentic AI platform on Azure using Node.js or .NET 8, designing APIs, data pipelines, vector search, and agent tools with LangChain and FastAPI.
Design and build multi-agent AI systems using AutoGen, CrewAI, or LangGraph for enterprise automation, integrating tool-calling workflows and RAG with production-grade reliability.
Build and scale an AI-driven security platform by designing autonomous agents for threat investigation, response, and automation, while architecting distributed systems for high-scale, multi-tenant security operations.
Build and improve AI agents that automate cybersecurity workflows like SOC alert triage, threat hunting, and penetration testing using Python, LLMs, and distributed systems.
Builds enterprise AI apps using LLMs, RAG, and Agentic AI frameworks in Python, deploys them on cloud platforms, and integrates them with enterprise systems.
Designs and deploys production-grade generative AI solutions (LLMs, RAG, AI agents) for enterprise clients, using frameworks like LangChain and cloud platforms like Azure OpenAI, with a focus on scalability, security, and cost efficiency.
Build and scale enterprise microservices in Java and React, then extend them with agentic AI features that securely connect LLMs to internal systems using RAG, MCP, and prompt engineering.
Senior full-stack engineer building Java microservices and AI agents that connect LLMs to enterprise systems using RAG, prompt engineering, and modern AI tooling.
Build and deploy AI applications and agentic workflows using LangChain/LangGraph, prompt engineering, and Python, while maintaining data pipelines and evaluation frameworks for a fintech firm.
Builds enterprise Java full-stack apps with Spring Boot, Angular/React, AWS, and AI integrations using Python, LangChain, and LangGraph.
Build and deploy production-grade AI agents for a media company, designing agentic workflows, RAG systems, and LLM-powered tools to automate editorial, subscriptions, and advertising tasks.
£100,000.00 GBP 10% Bonus Onsite WORKING Location: Newcastle Upon Tyne, North East - United Kingdom Type: Permanent AI Engineer Salary: Up to £100,000.00 + Excellent Benefits Location: Newcastle (Onsite) We're…
We couldn't check your fit for this role — add a CV to your profile to see it next time.