Every company wants AI engineers. Very few know how to find ones with the right domain expertise, verify they can actually deliver, and make sure they’ll stay. Whether you’re a startup shipping your first model, an IT services firm staffing client engagements, or an enterprise modernising at scale — we solve that.
We match AI skills to your industry context — not just stack keywords.
Qualified, interviewed, technically assessed candidates.
We’ve built AI companies. We evaluate like co-founders, not recruiters.
The AI talent market is broken in different ways for different buyers. But the root cause is the same.
Plenty of people list PyTorch on their CV. Very few can apply it to your specific domain — whether that’s fintech fraud detection, clinical NLP, telecom network optimisation, or mining predictive maintenance. Without domain context, AI hires ship demos, not products.
Traditional staffing firms match resumes to keywords. They can’t tell the difference between someone who completed a Kaggle competition and someone who shipped an LLM system to production. For IT services delivery leads and enterprise CIOs alike, that gap is expensive.
The most expensive AI hire isn’t a bad one — it’s a misaligned one. Someone technically strong who doesn’t actually want this role, or whose career goals don’t match the engagement. They leave in six months and you’re back to square one, mid-project.
Different buyers, different pain points. One shared need: AI talent that’s technically verified, domain-relevant, and ready to deliver.
You’re building AI-native products or adding AI to an existing platform. You need engineers who can go from research to production — fast — without the FAANG salary arms race.
Your clients are demanding AI and GenAI capabilities. Your bench is full of strong engineers who weren’t trained for ML workloads. You need production-ready AI talent to staff client projects — without the bench cost.
Banking, insurance, telecom, mining — you’re modernising at scale but the local AI talent pool is thin and internal hiring takes months. You need a partner who understands both the technology and the risk tolerance.
We talk to the hiring manager — not just about the job spec, but about what they’re actually trying to accomplish. What does success look like? What’s the team dynamic? Whether it’s a client delivery engagement, a product build, or an enterprise transformation — the job description is the starting point, not the answer.
We don’t just search for “ML engineer.” We search for someone who can build recommendation systems for e-commerce, deploy NLP pipelines in healthcare, architect RAG systems for enterprise, or build predictive maintenance models for industrial operations. The domain match is what separates a hire from a mis-hire.
We speak to every candidate. We assess technical depth, career expectations, and motivation. A candidate who’s overqualified and bored will leave in six months. A candidate whose career goals don’t align will never perform. We screen for fit, not just skill.
We don’t outsource evaluation. Our founding team has built AI products, scaled engineering organisations, and shipped production systems. We assess system design, code quality, and problem-solving judgment the way a senior engineering leader would — because we are.
Before we present anyone, we confirm the candidate is genuinely motivated for the role and it fits their career trajectory. Happy candidates stay longer, ramp faster, and deliver more. Forced placements don’t.
You get a shortlist — not a stack of resumes. Every candidate has been interviewed, assessed, and matched against your actual goals. We stand behind every placement because we’ve done the work to get it right.
From applied ML to LLM infrastructure to AI leadership — we place the roles that actually ship AI products.
Our founding team has built and shipped AI products in healthcare, enterprise software, and automation. We evaluate AI talent the way technical co-founders do — because we are.
An ML engineer who built fraud detection for fintech isn’t the same as one who built claims automation for insurance. We match the domain context, not just the technology stack.
We verify that every candidate genuinely wants this role and it fits their career path. That’s how you avoid the six-month resignation cycle that plagues AI hiring across startups, IT services, and enterprise alike.
We don’t screen resumes for keywords. We assess whether someone can actually build great AI systems in your specific context — whether that’s a client delivery engagement, a product build, or an enterprise transformation.
Three markets, three buyer profiles. Our multi-market presence enables optimised cost, quality, and timezone strategies tailored to how you hire.
Senior AI engineers, ML leads, and AI executives for tech startups and enterprises building or scaling AI-native products. For teams that need talent on-site or US-timezone-aligned.
Production-ready AI and ML engineers for IT services firms staffing client engagements. Pre-vetted contract talent that deploys directly — no bench cost, no training ramp.
AI talent for large enterprises in banking, insurance, telecom, and mining — industries modernising at scale where the local AI talent pool is extremely thin.
Tell us what you’re building, staffing, or transforming. We’ll come back with candidates who can actually do it.
Start a Conversation →