AI Machine Learning Engineer

AI Machine Learning Engineer

Day rate - £420 per day (Inside IR35)

Contract length - 6 months (extensions for up to 2 years)

Hybrid working - x2 days per week in Manchester

Spencer Rose are partnered with a leading Financial Services organisation who are currently on the look out for AI Machine Learning Engineer to design, build and deploy enterprise scale AI solutions. Within this role, the AI Machine Learning Engineer will work with cutting edge technologies including LLMs, Agentic AI, RAG, PyTorch and Kubernetes, to deliver production ready applications.

The AI Machine Learning Engineer will have the following responsibilities

• Design AI applications using LLMs, transformer models, RAG and Agentic AI patterns.
• Build AI agents with reasoning, memory, workflow orchestration and tool integration capabilities.
• Develop and optimise machine learning and deep learning models using Python and PyTorch.
• Create embedding, indexing and semantic retrieval pipelines to support enterprise knowledge solutions.
• Deploy and manage models using Docker, Kubernetes, KServe and Vertex AI.
• Build CI/CD, model training, monitoring and evaluation pipelines following MLOps best practices.
• Manage model versioning, registries and release processes in line with governance standards.
• Monitor model performance, drift, latency, fairness and service reliability.
• Optimise inference performance, scalability and cloud infrastructure costs.
• Support progressive deployment strategies including canary, shadow, blue/green releases and A/B testing.
• Collaborate with Product, Data Science, Platform, Security and Architecture teams to establish AI engineering standards.

The AI Machine Learning Engineer will need to have the following skills/experience

• Strong experience developing and deploying Machine Learning and Generative AI solutions.
• Expertise in Python, PyTorch, LLMs and RAG architectures.
• Experience with Docker, Kubernetes and cloud-based AI platforms.
• Strong understanding of MLOps, model monitoring, CI/CD and AI governance

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