AI Engineer

AI Engineer

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 .

This is a 12 month temporary contract to start asap

Day rate:
Competitive Market rate

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.

• 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 ope rationalise 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.

Essential

Skills:

• 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.

Desirable

Skills:

• Experience with in 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 Lang Chain, Lang Graph, Llama Index, CrewAI, Semantic Kernel, or similar frameworks.
• Familiarity with vector databases such as Pinecone, Weaviate, ChromaDB, or pg Vector.
• 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).

Due to the volume of applications received,…