Senior engineers, inside your team
Specialist AI, cloud, and full-stack engineers embedded into your existing team — working in your tools, your ceremonies, and your codebase. IP protection built in from day one.
The specialist engineering gap is a real constraint
faster task completion for developers using AI-assisted tooling
McKinsey—Economic Potential of Gen AIWhy specialist hiring alone is not the answer
The challenges organisations bring to us when they need specialist engineering capacity without the cost and risk of traditional hiring.
Specialist AI and cloud engineers take months to hire
- Senior AI and ML engineers are globally scarce and in high demand
- Traditional hiring takes three to six months — too slow for delivery windows
- Interview loops, offer negotiations, and notice periods add more delay
- Every missed week is lost product velocity you cannot recover
- Embedded specialists can be contributing within five to seven days
High attrition means knowledge leaves with the engineer
- Departing engineers take architectural decisions and context with them
- Tribal knowledge accumulated over months disappears overnight
- Without redundancy, a single departure derails the whole workstream
- Re-onboarding a replacement takes months of lost productivity
- Embedded squads with built-in peer review reduce single-point-of-failure risk
Specific skill gaps are blocking product roadmap progress
- Modern products need AI, cloud, full-stack, and data engineering together
- Most teams have some specialisms but rarely all of them
- A single missing skill creates a bottleneck across the entire roadmap
- Hiring for every gap is uneconomical and too slow
- Embedded specialists close specific gaps without growing permanent headcount
New hires take three to six months to be fully productive
- Even senior engineers need months to understand codebase and domain
- Without structured onboarding, ramp time is unpredictable and costly
- Time spent onboarding is time not spent delivering roadmap items
- Domain knowledge gaps lead to architectural decisions that age poorly
- CodeCones specialists follow a five-day structured onboarding before billing begins
Freelancers introduce IP and quality risk
- Marketplace engineers operate under weak NDAs with no enforcement
- Personal devices mean your codebase is in environments you cannot control
- No peer review means quality depends entirely on a single individual
- Your software — your most valuable asset — is at risk
- CodeCones specialists use managed devices with enterprise NDA from day one
External engineers take time to integrate into your culture
- Separate delivery teams create coordination overhead and integration risk
- Handover at end of engagement is a structured knowledge loss event
- Your team loses ownership when delivery is external
- Embedded engineers join your ceremonies, tools, and codebase directly
- No handover problem — the knowledge stays with your team throughout
How it works
Delivery ownership stays with you
We embed engineers into your team without changing your delivery model. You keep ownership; we fill the capability gaps.
You retain delivery ownership
Your team, your process, your tools
We embed senior specialists
The capability you are missing, today
Tools and technology
Specialists across the modern engineering stack
We provide senior engineers with deep expertise across AI, cloud, full-stack, and data — matched to your specific technical requirements.
AI and ML
- LLM Integration
- RAG Pipelines
- MLOps
- Fine-Tuning
Cloud and Platform
- AWS / Azure / GCP
- Kubernetes
- Terraform
- CI/CD
Full-Stack
- React / Next.js
- Node.js / Python
- Go / TypeScript
- API Design
Data Engineering
- Spark / Airflow
- BigQuery / Redshift
- Kafka
- Data Modeling
DevOps
- GitHub Actions
- ArgoCD
- Observability
- Security Scanning
Security
- Penetration Testing
- SAST / DAST
- Compliance Architecture
- Secrets Management
Technology
Technologies our embedded specialists work with
A sample of the platforms, frameworks, and tools our engineers use across active engagements.
Technology selection is guided by project requirements, existing environments, and client preferences. This list is not exhaustive.
Why CodeCones
Embedded specialists vs. the alternatives
Traditional approaches
Slow hiring
Three to six months to hire a specialist engineer, if you can find one
No IP protection
Freelancers on personal devices with generic NDAs and no audit trail
Knowledge silos
One engineer per skill area — when they leave, the knowledge leaves with them
Long ramp time
New hires take months to be productive without structured onboarding
Embedded Engineering Specialists
Contributing in 5–7 days
Structured onboarding means engineers ship meaningful work in their first sprint
Enterprise IP governance
NDA, managed device, access controls, and audit trail from day one
Built-in redundancy
Knowledge shared across the CodeCones network — no single points of failure
Technically interviewed
Every engineer passes our technical interview — no CV matching or junior placements
Outcomes and case studies
What embedded engineering looks like in practice
Representative examples of the types of outcomes organisations achieve when they embed CodeCones specialists into their delivery teams.
Challenge
A trading platform needed AI-assisted risk classification but could not hire ML engineers fast enough to meet a regulatory deadline.
AI capability delivered in 6 weeks
Two embedded ML engineers integrated into the existing team, built and shipped the classification pipeline within the required window — with zero recruitment overhead.
Challenge
A growing SaaS company needed to close a cloud architecture skills gap after losing their lead platform engineer to a competitor.
Zero recruitment cost, zero context loss
An embedded cloud architect joined within five days, stabilised the platform infrastructure, and documented architecture decisions to prevent future knowledge loss.
Challenge
A health-tech team needed AI-powered document extraction for patient intake but had no ML capability internally.
3 months to production from zero ML capability
An embedded AI engineer integrated into the product squad and delivered a production extraction pipeline meeting clinical-grade accuracy requirements.
Representative outcomes. Individual results depend on team size, complexity, and engagement scope.
Industry expertise
Industries we serve
Healthcare
Clinical AI, patient workflow automation, and administrative data platforms.
Financial Services and Insurance
Fraud detection, compliance automation, and intelligent claims workflows.
Retail and E-commerce
Personalisation engines, inventory forecasting, and AI-driven customer operations.
Technology and SaaS
AI-native product features, platform engineering, and SaaS scalability.
Manufacturing and Logistics
Predictive maintenance, supply chain intelligence, and computer vision QA.
Travel and Hospitality
Dynamic pricing, guest personalisation agents, and revenue intelligence.
Get in Touch
Tell us about your team and what you need
We'll match you with the right engineers and walk you through how the embedded model would work for your context.
- Dedicated project manager from day one
- Fixed-scope or continuous engagement options
- Full IP ownership — all deliverables are yours
- Response within one business day
FAQs