Insight International (UK) Ltd
AI/ML Engineer

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Job Description: Senior AI / ML Engineer-6+ months contract
Location: Manchester, UK
Type: Hybrid
ROLE PURPOSE
Design, build and operate production-grade AI and machine learning solutions across the full lifecycle. The role combines Generative AI and Agentic AI engineering with MLOps, scalable model serving, cloud infrastructure, production monitoring, and responsible engineering controls.
Key Responsibilities
- Design enterprise AI applications using Large Language Models, transformer architectures, Retrieval-Augmented Generation (RAG) and Agentic AI patterns such as ReAct.
- Build agents that use tools, reasoning, memory, and workflow orchestration; integrate AI capabilities with enterprise platforms through secure APIs and microservices.
- Develop, evaluate, and optimize machine learning and deep learning solutions using Python and PyTorch, taking work from experimentation through production.
- Create embedding, indexing, semantic retrieval, and ranking pipelines for grounded AI responses and enterprise knowledge use cases.
- Package models using Docker and deploy through KServe, Vertex AI endpoints, and Kubernetes-based serving platforms.
- Build CI/CD, continuous training, and continuous monitoring workflows, including model evaluation and controlled promotion across environments.
- Operate feature and model registries, model versioning, and reproducible release processes aligned to governance and risk controls.
- Monitor model quality, data quality, drift, latency, fairness signals, infrastructure health, and service-level objectives.
- Optimize inference performance and cost through autoscaling, GPU scheduling, resource management, and cloud-native architecture.
- Enable safe progressive delivery using canary, shadow, and blue/green deployments, rollback controls, and A/B testing.
- Collaborate with Product, Data Science, Platform, Architecture, Security, and Risk teams; establish reusable patterns and engineering standards.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Mandatory Skills and Experience
Capability
Required experience
Core engineering
- Strong production Python engineering, API development, automated testing, and asynchronous or distributed service patterns.
Deep learning
- Hands-on PyTorch experience and strong understanding of transformer architecture, inference, and model evaluation.
Agentic AI
- Experience with ReAct or comparable agent patterns, including tool calling, memory, reasoning, and orchestration.
RAG & retrieval
- Production RAG, embeddings, chunking, indexing, vector search, retrieval/ranking, and grounding techniques.
Google Cloud
- GCP knowledge with Vertex AI, cloud compute/storage, and cloud databases; GKE experience is advantageous.


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Containers & serving
- Docker plus model packaging and serving using KServe, Vertex AI endpoints, or equivalent Kubernetes-native platforms.
MLOps lifecycle
- Feature/model registries, CI/CD, continuous training, model monitoring, version management, and drift detection.
Scale & release
- Cost-efficient autoscaling, GPU scheduling, canary, and shadow deployment, rollback strategies, and A/B testing.
Other experience
- LangChain, LangGraph, LlamaIndex, or comparable orchestration frameworks.
- Kubernetes/GKE, Harness, or equivalent delivery tooling, GitOps, and infrastructure automation.
- Prometheus, Grafana, Dynatrace, or similar observability platforms.
- Delivery in a regulated enterprise with security, privacy, model risk, and responsible AI controls.
- Technical leadership, architecture reviews, mentoring, and cross-functional stakeholder collaboration.
What Success Looks Like
- AI services move safely from experiment to reliable, monitored production with repeatable delivery controls.
- Model serving is secure, resilient, scalable, and cost-efficient, with measurable quality and operational performance.
- Reusable patterns improve delivery speed while supporting transparency, governance, and risk management.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London