Interested in this AI Consultant role at Omnea?
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Our Mission
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At Omnea, we’re reinventing how enterprise businesses operate, starting with the most painful parts: procurement – where a single purchase can drag on for months, trigger 50\+ emails, and pull in Finance, Legal, Security, and IT just to get something approved.
We’ve raised $75M from Khosla Ventures, Insight Partners, and Accel to change that. Our AI\-native platform connects every person, step, and system so buying is fast, safe, and efficient – one place to request, automated approvals and renewals, real\-time supplier risk, and complete spend visibility.
The opportunity is massive. Every enterprise on the planet has this problem and nobody has solved it. We’ve 10x’d ARR to double\-digit millions in 18 months and are trusted by global enterprises like Spotify, MongoDB, Monzo, and Albertsons. We’re now the 4th fastest growing startup in Europe \& the Sunday Times' \#1 Best Medium Sized Tech Company To Work for.
Our team previously scaled Tessian (cybersecurity tech, backed by Sequoia, Balderton, Accel, acquired post\-Series C), and our team includes ex\-founders operators who’ve grown unicorns, shipped world\-class products, and executed at the highest levels. You’ll work alongside leaders like Ben, Abs, Sabrina, and Rebe.
Find out more about the team and life at Omnea here.
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The Role
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As an Applied AI Consultant, you'll work directly with our most strategic customers to understand their objectives and key challenges, identify high\-impact AI opportunities, and translate them into measurable business value.
You'll be building AI solutions on Omnea's procurement automation platform, combining the structured data flowing through requests, approvals, contracts, and supplier records with AI. Omnea gives AI the operational context of how a business actually buys, so it can move past answering questions and start taking action inside procurement itself: routing requests, flagging risk, drafting contract terms, negotiating with suppliers. There is no AI without that context.
You'll prototype these solutions, demonstrate their value to executives, and ensure successful implementation, adoption, and value realisation, growing Omnea's footprint at those customers as you go.
This customer\-facing role is ideal for people who enjoy working directly with customers and turning complex business challenges into practical AI solutions. The focus is on problem framing, solution design, and business impact rather than pure implementation. While you will prototype AI solutions, the primary goal is to deliver meaningful business value to customers.
What You'll Do
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- Identify High\-Impact AI Opportunities: Work closely with enterprise customers to understand their strategic priorities and operational challenges, identifying where AI can deliver measurable business value.
- Design AI\-Powered Solutions: Translate complex business problems into scalable solution architectures, using Omnea's AI to eliminate manual work across procurement.
- Prototype and Demonstrate Value: Rapidly build prototypes and proof\-of\-value solutions that demonstrate tangible outcomes to business and technical stakeholders.
- Lead AI Innovation with Customers: Facilitate workshops, hackathons, and innovation sessions with customers to explore new ways AI can transform their operations.
- Drive Adoption and Business Impact: Partner with customer teams to ensure successful implementation, adoption, and realisation of measurable value from AI deployments.
- Develop Domain Expertise: Build deep expertise in specific industries or business domains (e.g., supply chain, finance) and help scale that knowledge across the organisation.
What's Different About This Role
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- You'll spend more time on problem framing and validation than on hand\-building every proof of value. AI can generate a working prototype fast; your job is deciding whether it actually solves the customer's problem.
- Writing a sharp brief for what you want AI to build is now core to the role, not a side skill.
- Judgment on business impact matters more than raw build speed: which use case actually moves the metric the customer cares about, and which is just an impressive demo.
- You'll keep pushing on what's possible as Omnea's AI tooling evolves, and bring what you learn back to the rest of the team.
About You
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You might be a great fit if you…
- have 5\+ years of experience in solutions consulting, data / AI consulting, technical pre\-sales, sales engineering, value engineering, or customer\-facing solution\-architecture or AI roles
- enjoy translating business challenges into technical solutions
- can communicate complex technical concepts to business and executive stakeholders
- have a good understanding of business processes across procurement, finance, and supply chain, with the ability to translate high\-level business needs into specific AI use cases
- demonstrate strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos
- know how to measure the business impact of what you've delivered, and can turn that into a clear story for stakeholders \- time saved, error rates down, adoption up, whatever the metric that actually matters to that customer
- have hands\-on experience identifying, designing, and building automations or AI\-driven solutions for repeatable processes, not just scoping them on paper
At Omnea, we embrace diversity. To build a product that's loved by everyone, we're best served by a team with all sorts of backgrounds, experiences, and perspectives. We encourage you to apply even if your experience doesn't quite match the full job spec! And regardless of your race, religion, colour, gender, or anything else! If you think you could be a good fit for Omnea, please reach out.
A few things to note:
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- We offer competitive geo\-localised benefits, and you can check out our UK Benefits Package here and our US Benefits Package here.
- We work Tuesdays, Wednesdays \& Thursdays in\-person at our offices. At this early stage of our company life\-cycle it's important to us that we get this together\-time, and you can read more about why we believe this is a winning move here
- We're commercial, ambitious and we don't pretend otherwise! We're actively seeking folks looking to make the most of a career\-defining opportunity, with the hunger to be part of building something really impressive. You can see our values here and our Omnea Future Founder's fund here!
- We sometimes use AI note\-takers to help us transcribe interview notes, so we can be more present in your interview. If you'd like to opt out of us using automatic transcribers, please note this in the free text field in your application, otherwise we'll take your application as confirmation that you're happy for us to use notetakers (whether added to video calls or in the background).
We are proud to be recognised for both our culture and product, and we are just getting started. Join us as we grow!
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Additionally, where roles have hard\-specified requirements (e.g. \[x] days in office, unable to provide visas, etc), if in your application you provide deterministic check\-box confirmation that you do not meet the hard\-specified requirements, deterministic (not AI or subjective) automatic rejection criteria are in place.
Compensation Range: $120K \- $190K
Salary Context
This $120K-$190K range is above the 75th percentile for AI Consultant roles in our dataset (median: $130K across 5 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Consultant positions make up 0% of the market. At Omnea, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills in Demand for This Role
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Consultant roles pay a median of $152,604 based on 12 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $120K to $190K.
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.
Omnea AI Hiring
Omnea has 1 open AI role right now. They're hiring across AI Consultant. Based in NY, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Consultant roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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