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# AI Engineer

- **Company:** Project Recruit
- **Location:** London, UK (Remote available)
- **Contract:** Temporary contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Engineering, Continuous Integration, Information Engineering, Distributed Systems, Monitoring of Systems, Python (Programming Language), Enterprise Messaging Systems, Open Source Technology, Performance Tuning, Reliability Engineering, Search Technologies, Enterprise Data Management, Enterprise Application Integration, Pinecone, Feature Store, Google Cloud, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Llamaindex, Generative AI, Agentic-AI, Event Driven Architecture, Pgvector, Containerization, AI Platforms, Kubernetes, Deployment Automation, CrewAI, Weaviate, Machine Learning Operations, Google Gemini, Restful APIs, Model Context Protocol, Semantic Kernel, Docker, ChromaDB, Microservices
- **Published:** October 12, 2026
- **Apply:** https://www.careerboard.com/gb/en/find-jobs-in-United-Kingdom/-2BF08ACB2F5E3AE610/

## About the Role

* Strong hands-on experience in Generative AI, Agentic AI, and Large Language Model (LLM) implementations.
 * Proven expertise with Google Gemini and Open-Source LLM ecosystems.
 * Advanced Python development skills with experience building enterprise-grade applications.
 * Experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
 * Strong understanding of ADK, MCP, AI orchestration frameworks, and integration patterns.
 * Experience developing REST APIs, microservices, and distributed applications.
 * Hands-on experience with Google Cloud Platform (GCP) services and cloud-native architectures.
 * Strong knowledge of MLOps practices, CI/CD pipelines, model life cycle management, and automation.
 * Experience with Docker, Kubernetes, container orchestration, and scalable deployment strategies.
 * Excellent analytical, problem-solving, stakeholder management, and communication skills.

Desirable Skills:

 * Experience within financial services, banking, insurance, or other regulated industries.
 * Expertise in GenAI, Gemini, and Open-Source LLMs, including fine-tuning and onboarding.
 * Strong experience in Python, RAG, ADK/MCP, APIs, and microservices.Skilled in GCP, MLOps, CI/CD, Docker, Kubernetes, and scalable AI solutions.
 * Proficient in observability, performance optimization, security, and production reliability.
 * Knowledge of Responsible AI, AI governance, compliance, and model risk management.
 * Experience with LangChain, LangGraph, LlamaIndex, CrewAI, Semantic Kernel, or similar frameworks.
 * Familiarity with vector databases such as Pinecone, Weaviate, ChromaDB, or pgVector.
 * Experience implementing observability and monitoring solutions for AI platforms.
 * Understanding of event-driven architectures, messaging platforms, and enterprise integration patterns.
 * Exposure to data engineering, feature stores, and enterprise data platforms.
 * Experience leading technical design discussions and architecture reviews.
 * Knowledge of AI security, prompt engineering, guardrails, and reliability engineering practices.
 * Relevant cloud and AI certifications (Google Cloud, Kubernetes, Generative AI, or ML certifications).

## Description

You will work with cutting-edge technologies including Google Gemini, Open-Source Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agent Development Kits (ADK), Model Context Protocol (MCP), and Google Cloud Platform (GCP). The role provides exposure to cloud-native architecture, MLOps, AI governance, and enterprise-scale solution delivery, enabling you to contribute to transformational programmes while remaining at the forefront of AI innovation., * Design, develop, and implement scalable Generative AI and Agentic AI solutions using Gemini and Open-Source LLMs.

 * Build and optimise RAG-based applications leveraging enterprise knowledge sources and vector search technologies.
 * Develop AI-powered APIs, microservices, and reusable components to support business-critical applications.
 * Fine-tune, evaluate, onboard, and operationalise foundation models to meet specific business requirements.
 * Collaborate with business stakeholders, architects, and engineering teams to translate use cases into technical solutions.
 * Deploy and manage AI workloads on GCP using modern cloud-native and container technologies.
 * Implement MLOps best practices covering model deployment, monitoring, CI/CD, automation, and governance.
 * Ensure solutions meet enterprise standards for security, reliability, scalability, and performance.
 * Conduct performance tuning, observability monitoring, and root cause analysis for production systems.
 * Provide technical leadership, mentoring, and support for AI engineering best practices across delivery teams.