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About This Role
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.
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.
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
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Keyrus Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Keyrus Group AI Hiring
Keyrus Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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