人工智能工程师

临时
伦敦
发布于两周前
可协商英镑/年

人工智能工程师

Our client, a leading global supplier for IT services, requires AI Consultant/Engineer to be based at their client’s office in Cardiff / Manchester / London, UK.

This is a hybrid role – you can work remotely in the UK and attend the Cardiff / Manchester / London office 2 days per week .

这是一份为期12个月的临时合同，需尽快入职

日费率：具有市场竞争力的费率

Join a high-performing engineering team delivering next-generation AI and digital transformation solutions within the financial services sector. This role offers the opportunity to design, build, and deploy enterprise-scale Generative AI and Agentic AI solutions that enhance customer experiences, improve operational efficiency, and accelerate business innovation.

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.

主要职责

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.

关键要求

核心技能：

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 lifecycle management, and automation.
Experience with Docker, Kubernetes, container orchestration, and scalable deployment strategies.
Excellent analytical, problem-solving, stakeholder management, and communication skills.

理想技能：

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).