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Why Keyrus, Why Now!
Keyrus is an international group of 2,800 consultants and experts across 28 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have been building the data foundations that make intelligent systems work - designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalise intelligence.
At Keyrus, you will not simply develop technical skills. You will strengthen the judgment required to understand complex environments, make sound decisions, and design systems that create sustainable value. Over time, you grow into a professional who bridges data, AI, and human decision-making at scale - a Keyrus Architect of Intelligence.
Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.
Location: Remote
Type of Contract: Fulltime Employee
Role level: Experienced individual contributor
What You'll Architect
As a Forward Deployed AI Engineer, you work at the heart of a client's most pressing AI challenges, turning intent into an operational, measurable result in weeks rather than months. This is an experienced individual-contributor role for someone who combines hands-on engineering, architectural judgment, and business understanding.
You own the problem from ambiguity through to execution - understanding the real context, building and deploying the solution, and proving, not declaring, that it creates value. Once the terrain is understood, you become the reference the team relies on to make that result last.
Your Responsibilities
- Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on delivery.
- Locate, qualify, and secure access to the data required for each use case, working directly with the Data Engineer.
- Translate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows.
- Prototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain experts.
- Work with Data Engineers, Software Engineers, Foundations Architects, Governance experts, Business Value Advisors, and Service Delivery Managers to deliver sustainable outcomes.
- Balance speed, quality, cost, security, and maintainability while making clear technical and delivery trade-offs.
- Define success criteria from the outset, including adoption, performance, reliability, risk, cost, and measurable business value.
- Ensure solutions are documented, governed, and transferable so clients can operate them with confidence.
- Turn successful delivery into reusable patterns, accelerators, and building blocks that strengthen future engagements.
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.
Who You Are
You are a hands-on engineer who thinks like an architect and acts like a builder. You are comfortable working closely with the client, the problem, and the delivery, and you make sound decisions in complex, evolving environments.
- You enjoy solving operational challenges, not only exploring technical concepts.
- You communicate clearly with both technical teams and senior business stakeholders.
- You navigate ambiguity with confidence, validate assumptions, and adapt quickly.
- You take ownership of outcomes and raise risks or changing priorities early.
- You understand that AI value depends on the full system: data, workflows, governance, adoption, and measurement.
- You naturally look for what can be reused, improved, and scaled.
What You Bring
Experience
- Typically 5-10 years of relevant experience in AI Engineering, Machine Learning, Software Engineering, Data Engineering, or technical consulting.
- Hands-on experience delivering AI, GenAI, or software solutions into production.
- Experience working directly with clients or in complex stakeholder environments.
- Evidence of turning complex use cases into adopted measurable solutions.
- A degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field - or equivalent practical experience.
Technical Skills
- Strong Python development skills, API integration experience, and modern software-engineering practices.
- Hands-on experience with Large Language Models, GenAI architectures, prompt workflows, and model/provider selection.
- Experience with RAG, embeddings, vector search, AI agents, and agentic workflows.
- Familiarity with frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, AutoGen, or comparable tools.
- Experience integrating AI into enterprise systems, APIs, and business workflows.
- Experience with at least one major cloud platform: Azure, AWS, or GCP.
- Working knowledge of Docker, Git, CI/CD, production deployment, monitoring, and evaluation.
- Understanding of MLOps / LLMOps, security, data privacy, governance, and responsible AI principles.
Nice to Have
- Experience with multimodal models, fine-tuning, model adaptation, or open-source LLMs.
- Front-end or full-stack development experience, for example, Node.js or React.
- Consulting or professional-services experience.
- Exposure to regulated industries or enterprise governance requirements.


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What Makes You Successful
- You combine technical credibility, pragmatism, and end-to-end ownership.
- You focus on real-world outcomes, adoption, and measurable value - not only the solution itself.
- You move quickly while balancing speed, quality, cost, and risk.
- You build trust and become a reliable partner in complex client environments.
- You operate effectively under pressure in client environments, where progress and results are continuously visible.
- You know when to go deep technically and when to orchestrate the right expertise.
- You continuously improve, reuse, and scale what works across engagements.
What We Stand For
- Collective Intelligence - We combine expertise, functions, and geographies to solve complex client challenges.
- Reliability - We deliver complex work with rigour and build lasting client trust.
- Pragmatism - We prioritise concrete impact and measurable value over abstract technological discourse.
- Entrepreneurial Spirit - Curiosity, energy, and freedom enable initiative and sustained innovation.
Our Hiring Process
We keep our process straightforward and respect candidates' time. The local process should normally include:
- A conversation with the Talent team to discuss the candidate's background, expectations, and what Keyrus can offer.
- A technical and delivery-focused conversation with the hiring team, using a real or representative challenge.
- A final conversation with senior leadership or the relevant business lead to discuss values, culture, and longer-term ambitions.
ABOUT KEYRUS
At Keyrus, we help organisations move from experimental to industrialised AI, from isolated agents to orchestrated systems, and from insight to execution. As Architects of Intelligence, we design the Operating System of the intelligent enterprise, embedding intelligence into business processes so that it creates sustainable, measurable value. We operationalise intelligence.
Powered by our proprietary Human Orchestrated Model™ (HOM), we architect reliable:
- Intelligence Foundations
- Human in Command governance
- Performance Steering
With 30+ years of expertise and 2,800 employees across 28 countries, we build adaptive, resilient intelligent environments where technology amplifies human capability and performance compounds over time.
AI does not transform businesses. Architected intelligence does.
Keyrus is listed on Euronext Growth Paris. (ALKEY - ISIN Code: FR0004029411 - Reuters: KEYR.PA - Bloomberg: ALKEY: FP).
For more information: www.keyrus.com
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