Interested in this AI/ML Engineer role at Keyrus USA?
Apply Now →Skills & Technologies
About This Role
Why Keyrus, Why Now!
Keyrus is an international group of 3,000 experts across 30 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does.
For more than 30 years, we have helped organizations build the data foundations that power intelligent systems. Today, we go further by designing and operationalizing intelligence that delivers measurable business value.
Building AI capabilities is only part of the challenge. Real transformation happens when those capabilities work within existing business systems, data environments, workflows, and processes.
At Keyrus, you'll work closely with clients to design the architecture and technical foundations that turn AI opportunities into secure, scalable, production-ready solutions.
Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.
Role details
- Location: United States (Remote)
- Contract: Permanent, full-time
What you'll architect
As an AI Foundation Architect, you'll design the technical foundations that enable organizations to develop, deploy, and scale AI and agentic solutions in enterprise environments.
You'll connect cloud infrastructure, data platforms, enterprise applications, models, agents, and security capabilities to create reliable architectures that support business needs.
Your focus goes beyond solution design. You'll define architecture principles, make key technical decisions, and guide engineering teams in building secure, scalable, and cost-effective AI systems.
Your responsibilities
- Lead architecture discovery with clients to understand business objectives, existing systems, technical constraints, and AI requirements.
- Define target architectures for production AI and GenAI solutions, integrating models, agents, cloud services, enterprise data, applications, and APIs.
- Own key architecture decisions within the assigned scope, establishing reference patterns, technical boundaries, and design standards.
- Design secure and reliable foundations addressing identity, access, scalability, resilience, observability, and operational requirements.
- Evaluate architecture trade-offs, including model selection, performance, latency, infrastructure costs, and AI consumption patterns.
- Guide AI Engineers, Data Engineers, Software Engineers, and cloud specialists to ensure consistency across technical implementations.
- Advise client stakeholders, review technical designs, and document reusable architecture patterns and decisions.
- Convert successful architecture decisions into reusable Keyrus assets, including reference architectures, decision records, standards, patterns, and design guidance that can strengthen future engagements.
Who you are
You are an experienced technical architect with a strong consulting mindset who combines strategic architecture judgment with recent practical understanding of production AI systems. You understand how cloud, data, applications, and AI technologies work together, and translate business requirements into practical architecture decisions. You are comfortable evaluating technical trade-offs, guiding engineering teams, and advising clients while respecting specialist ownership. You combine strong technical judgment with excellent communication, problem-solving, and leadership skills.
What you bring
Experience
- 8+ years of experience designing and owning complex enterprise, solution, cloud, or data architectures in production environments.
- Recent experience architecting production GenAI, LLM, or agentic systems, including integration with enterprise applications and operational infrastructure.
- Experience defining reference architectures, technical standards, integration patterns, and architecture decisions.
- Experience designing distributed systems and enterprise integrations across multiple technical environments.
- Ability to evaluate architecture trade-offs involving security, reliability, scalability, performance, maintainability, and cost.
- Strong client-facing consulting experience, including guiding multidisciplinary engineering teams and advising client architects, technical leaders, and business stakeholders.
Technical skills
- Deep architecture expertise in at least one major cloud ecosystem, such as AWS, Azure, or GCP, including relevant AI services.
- Experience designing model access and deployment patterns across managed AI services, APIs, open-source models, or comparable enterprise AI environments.
- Strong understanding of GenAI architectures, including LLMs, RAG, AI agents, model integration, and enterprise AI workflows.
- Experience with distributed systems, APIs, microservices, and enterprise integration patterns.
- Knowledge of enterprise data architectures, including lakehouse, streaming, data mesh, or feature stores.
- Understanding of security architecture, IAM, networking, resilience, observability, and production operations.
- Familiarity with MLOps, LLMOps, CI/CD, containers, infrastructure as code, and AI cost optimization.
Nice to have
- Experience with Amazon Bedrock, Azure AI, Vertex AI, or comparable cloud AI services.
- Experience designing multi-agent systems, model routing, or AI orchestration architectures.
- Familiarity with LangGraph, MCP, AI gateways, or similar technologies.
- Experience with Terraform, Kubernetes, or enterprise infrastructure automation.
- Experience with regulated environments, AI governance, or formal architecture review processes.
- Consulting or professional services experience across multiple clients.
What makes you successful
- You turn complex requirements into clear architecture solutions.
- You design secure, scalable, and cost-effective AI foundations.
- You guide technical teams and communicate effectively with clients.
- You make practical architecture decisions that support business value.
WHAT WE OFFER (KUS)
- A stimulating environment where you will be able to surpass yourself and discover new horizons.
- A strong culture of innovation and entrepreneurship.
- Health insurance for you and your family members.
- 401K plan.
- Monthly Wellness Allowance.
- 4 weeks of paid vacation;
- Language courses (French & English)
- Access to continuing education (in-house, conferences, events, courses, certifications, etc.)
The expected base compensation for this position ranges from $145,000 to $180,000 USD, depending on experience, skills, location, and internal equity. This salary range is provided as a guideline and may be adjusted for the selected candidate.
About Keyrus
At Keyrus, we help organizations move from experimental to industrialized 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.
Powered by our proprietary Human Orchestrated Model (HOM), we architect reliable:
- Intelligence Foundations
- Human in Command governance
- Performance Steering
With over 30 years of expertise and 3,000 experts across 30 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.
For more information: www.keyrus.com
Salary Context
This $145K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Keyrus USA, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($162K) sits 23% below the category median. Disclosed range: $145K to $180K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Keyrus USA AI Hiring
Keyrus USA has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $155K - $180K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.