Serving at scale
vLLM, TGI, quantization and GPU orchestration, with latency and cost per token as first-class metrics.
LLM INFRASTRUCTURE
Hire LLM engineers
From prototype to production: engineers who have served open and hosted models at scale — latency, evals and cost under control.
WHY CONECTIA
AI proficiency is one of our five vetting pillars
Every Conectia engineer is assessed on effective AI use. LLM specialists go deeper — into serving infrastructure, evaluation and €/token.
RAG that answers correctly
Retrieval design with measured faithfulness — evals and guardrails, not vibes.
Agent operations
Tool-use pipelines with human-in-the-loop and audit trails, ready for EU AI Act obligations.
3%of candidates pass CTO vetting
72hto a vetted match
7+years average experience
PROCESS · 72 HOURS
From discovery call to working engineer
No self-serve marketplace, no CV roulette: a CTO scopes the role with you and matches from a bench that already passed the hard filter.
Discovery with a CTO
Thirty minutes on your stack, constraints and definition of done — with an engineer, not a salesperson.
Bench match
We match against vetted seniors only. If the fit is not there, we say so instead of stretching a profile.
Match in 72 hours
The person for your context, with real code and architecture assessments attached — interviews optional, not mandatory.
14-day Pilot Sprint
Judge working output on your repo before any long-term commitment. Zero-risk by design.
LLM infra at scaleRAGvLLM · TGIEvalsGPU orchestrationLangGraph
DEPLOYMENT · THE FULL ARC
Staffing ends at the match. Deployment ends at the handover.
Marketplaces optimize the moment you accept a profile; everything after is yours to run. Every Conectia engineer ships with the full arc around them — not as a premium tier, but as the only way we place anyone.
Find
CTO-designed vetting passes 3% of candidates — and we present the person for your context, not a stack of CVs to interview through.
72h matchDeploy
Onboarding prepared before day one: access, context, the first week planned. A delivery manager runs the engagement end to end when the project calls for it.
Day-one planSustain
Check-ins every week — daily when the phase demands it — with you and with the engineer. Wrong fit? A substitute within 7 days, inside the 30-day guarantee, at no added cost.
7-day replacementHand over
The ending is a deliverable: full documentation, working accounts handed over, and a safe delete of corporate content — every credential accounted for.
Safe deleteRATES · ONE INVOICE
26–71% below an equivalent local hire
Location-based rates with everything included — you compare a single number against your local cost, not a fee maze.
- A single flat rate — compliance, payroll and EOR included
- Zero recruiting fees; 30-day replacement guarantee
- Start with a 14-day Pilot Sprint and judge the output, not the CV
FAQ
What CTOs and founders ask us
What does an LLM engineer do that an ML engineer doesn't?
The centre of gravity is systems, not training: serving infrastructure, retrieval, evals, guardrails and cost — making models useful and affordable in production.
Can you work with our private data and compliance constraints?
Yes — self-hosted or VPC deployments, EU data residency and audit trails are standard requirements for this bench.
Which stacks do your LLM engineers use?
vLLM/TGI serving, LangGraph-style orchestration, vector stores, eval harnesses, and the cloud/GPU layer underneath (AWS, GCP, K8s).
Do you build AI systems or also operate them?
Both: engineers build; if you want the day-to-day running owned, pair them with an AI Operator engagement.
How do you measure success on LLM work?
Eval scores, latency percentiles, cost per task and incident rates — agreed upfront, reported weekly.
Related: Staff Augmentation · Global engineering teams · Hire AI Operators · Hire Forward Deployed Engineers · Hire DevOps engineers