Talent We Offer

AI engineers who turn prototypes into reliable production systems

Own the full lifecycle from model selection to production deployment and monitoring
Partner with your product, data, and engineering teams to ship AI features safely and fast
Flexible engagement: targeted project delivery or embedded engineers augmenting your team

When hiring AI engineers through us makes sense

Signals that dedicated AI engineering capacity will materially accelerate your roadmap.

You have AI PoCs that never reach production

Experiments exist in notebooks or isolated repos, but you lack the engineering depth to harden, deploy, and maintain them at scale.

Your product roadmap includes AI features with no clear owner

Product and business teams are committing to AI‑powered capabilities, but no one senior is accountable for end‑to‑end technical delivery.

Your data science team is stretched on engineering

Data scientists can prototype models, but they’re blocked by infra, APIs, and MLOps work that slows down impact.

You need LLM expertise grounded in software engineering

You want to use LLMs and RAG patterns, but need engineers who understand latency, cost, security, and product integration—not just prompt engineering.

You can’t afford 6–9 month hiring cycles

Critical AI initiatives are waiting on hard‑to‑hire talent, and you need senior AI engineers in weeks, not quarters.

You want to de‑risk your first AI platform decisions

You’re choosing between cloud AI services, open‑source models, and tooling, and need practitioners who’ve built and operated similar systems before.

What AI engineers can deliver

High‑impact AI delivery scenarios

SaaS / Digital Platforms

Intelligent recommendations in your core product

Design and implement recommendation models (e.g. content, products, actions) with A/B testing and feedback loops, increasing engagement and conversion while keeping infra costs in check.

Retail / Supply Chain / Manufacturing

Forecasting and optimisation for operations

Build time‑series forecasting and optimisation models for demand, inventory, or capacity, integrating with existing planning tools to cut stockouts, waste, and manual planning effort.

Customer Service / B2C

LLM‑powered customer support automation

Implement retrieval‑augmented generation (RAG) and workflow orchestration to deflect tickets and assist agents, improving first‑contact resolution while maintaining compliance and guardrails.

Financial Services / Fintech

Risk and anomaly detection in financial flows

Develop detection pipelines combining rules and ML models to flag fraud, credit risk, or operational anomalies in near real time, reducing losses and investigation time.

Cross‑industry

End‑to‑end MLOps and model reliability

Set up CI/CD for models, feature stores, monitoring, and retraining workflows so AI systems are observable, auditable, and easy to evolve—not fragile scripts owned by one person.

How we staff and deliver

AI engineers embedded to ship, not just advise

We scope your AI goals, shape the right AI engineering profile, and integrate with your teams to deliver production‑grade systems—via focused projects or ongoing team augmentation.

Clarify outcomes, constraints, and stack

We align on business goals, data realities, compliance requirements, and your current architecture to define where AI engineering can create real leverage.

Shape the role and seniority mix

We specify skills (LLMs, classical ML, MLOps, data engineering, backend) and seniority, deciding whether you need a lead AI engineer, a delivery pod, or targeted specialist support.

Source, vet, and match AI engineers

We provide pre‑vetted AI engineers with strong software engineering fundamentals, evaluating them on problem‑solving, system design, and practical ML/LLM experience for your context.

Onboard and integrate with your teams

Engineers plug into your rituals, repos, and tooling, establishing clear ownership, delivery milestones, and collaboration patterns with product, data, and infra teams.

Deliver, iterate, and de‑risk handover

We ship increments to production with monitoring and documentation, support knowledge transfer, and can transition engineers off as your internal capability matures.

Business Outcomes

Faster path from AI ideas and PoCs to stable production features.
Reduced dependency on scarce internal experts for every AI initiative.
AI systems that are observable, maintainable, and aligned with your architecture.
Flexible capacity to ramp AI delivery up or down without long hiring cycles.

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