What skills do your AI engineers have?

Python, LangGraph and LangChain, RAG with Qdrant or pgvector, MCP tool development, n8n, FastAPI, fine-tuning with QLoRA and DPO, LLMOps with Langfuse and MLflow, and deployment on AWS, GCP or Azure. Each has shipped at least 1 LLM system that real users depend on.

What does it cost to hire an AI engineer through you?

$4,800 to $6,000 per engineer per month on engagements of 6 months or longer, depending on seniority and response-time commitments. That includes the founder’s architecture oversight.

Can we hire the engineer permanently later?

Yes, by agreement. Several engagements have ended with the client’s own team running the system and a light retainer for reviews, which is the intended end state.

Hire AI engineers

Hire AI engineers who have already shipped the system you are trying to build

Senior engineers placed inside your team on a monthly retainer, working in your repositories, your review process and your time zone. Every engineer has production RAG, agent or LLMOps work behind them, and the founder stays accountable for the result.

Book a free scoping call See the work

$4.8K to $6K: Per engineer per month
2: Engineers know every codebase
22+: Systems shipped to production
6 mo+: Typical engagement

Why this shape

What this looks like when the system has to survive production

Hiring a senior AI engineer in the USA or EU takes 3 to 5 months and a salary that starts at $180,000 before the recruiter fee. Most teams do not need a permanent hire; they need 1 or 2 people who have already built a production RAG system or an agent with guardrails, for the 6 to 12 months it takes to ship theirs.

That is what the retainer is. You get named engineers, not a bench. They work in your GitHub or GitLab, follow your review process, attend your standups, and report against metrics agreed in week 1. The founder reviews the architecture and stays on the engagement, so continuity never depends on 1 person. Several clients now run platforms their own teams took over, and keep us on a light retainer for architecture review.

What you get

Delivered as a working production system, in your cloud account

Named senior engineers

You interview and approve each engineer. No rotation without your agreement, no juniors billed as seniors.

Inside your process

Your repositories, your CI, your review rules, your standups. Documentation written as work happens, not at handover.

Your time zone

Working hours and a fixed weekly checkpoint set to your calendar, whether that is the USA, the UK or the EU.

Founder accountability

Architecture reviewed by the founder, who is reachable directly rather than through an account manager.

Continuity built in

2 engineers know every codebase, so leave, illness or a change of engineer never stalls the work.

Enterprise contracting

MSA, NDA and IP assignment from a registered IT services firm, USD invoicing, monthly billing you can stop with notice.

Architecture

Where the engineers work

Everything lives in your accounts: your repositories, your CI, your cloud. The engineers add capacity inside that setup rather than building somewhere you cannot see.

When this is the wrong tool

If the work is a 2 to 4 week scoped build, a fixed-price project is cheaper than a retainer. If you need 5 or more engineers for a year, hire them; we will help you interview.

Proof

Delivered systems closest to this work

Every figure comes from a system running in production for a real client.

Multinational enterpriseMulti-region · AWS

Search and automation across 5 million files that cannot leave their regions

1. Problem. 5 million+ files across regional data centers, S3, document systems, databases and application servers: contracts, financial statements, engineering reports, spreadsheets and email. The usual fix is 1 central index, and regional data rules ruled it out. So every cross-region question, and every workflow touching more than 1 system, depended on someone stitching answers together by hand.

2. Solution. The data stays in its region and the question travels to it. Retrieval runs locally and returns only what the asking user is already allowed to open. 7 LangGraph agents split the work, hand off to each other over A2A and reach each system through MCP servers with scoped permissions. Anything that changes a record waits for human approval, and n8n keeps ingestion, approvals and notifications moving around the clock.

LangGraphA2AMCPn8nOpenSearchLangSmith

Client name withheld under NDA. Ongoing 12-month program; figures are from the agreed evaluation set and the delivered milestones.

3. Impact

5M+: Files searchable across regions, permissions intact
97.86%: Task-success benchmark target on the agreed evaluation set
7: Specialised agents coordinating over A2A
12 months: Ongoing program: 6 delivered, 6 more contracted

DevOps and EngineeringUSA

An engineering org that shipped faster by spending less time fixing the pipeline

1. Problem. An engineering organization was spending more capacity managing its delivery pipeline than shipping product. Debugging was reactive, the same failure patterns kept returning across sprints, and incident postmortems were inconsistent when they happened at all.

2. Solution. A multi-agent platform that plugs into the existing DevOps toolchain. It reviews pull requests before merge, diagnoses pipeline failures with specific fixes, generates validated patches and test cases, watches deployments for anomalies, and writes structured incident summaries after every significant event.

LangGraphOpenAI Agents SDKGPT-5.6GitHub Actions

3. Impact

38%: Less debugging and incident resolution time
29%: Faster deployment cycles across environments
Fewer: Production regressions per sprint
Lower: Manual DevOps work per delivery cycle

Financial ServicesUSA

One governance layer for 5 production models in a regulated FinTech

1. Problem. 5 AI models were running in production with no shared visibility, no drift detection and no governance. Model behavior was tracked by hand in spreadsheets. In a regulated financial environment, that is an audit failure waiting to happen.

2. Solution. A single LLMOps control plane across all 5 models: scheduled automated evaluations, statistical drift detection against rolling baselines, shadow deployment and A/B testing before any version reaches live traffic, and a full model registry with SOC 2 compliant lineage and approval workflows.

MLflowAWS SageMakerArize PhoenixSOC 2

3. Impact

5: Production models unified under one platform
Auto: Drift detection replacing manual review
SOC 2: Audit-ready model lineage for compliance
3x: Faster safe promotion via shadow testing

Questions

hire AI engineers: what buyers ask first

AI agent development companyAn AI agent development company that ships the guardrails with the agent LLMOps consultingLLMOps consulting for teams that cannot say whether the system got worse last week AI consulting servicesAI consulting services from the engineers who would build it, not a slide deck Full service page

Usually within 2 weeks of the scoping call: 1 call to agree the role and metrics, an interview with the engineer, then onboarding into your repositories.

Your AI system should work, not just demo well

Book a free 30-minute scoping call. You leave it with a recommended approach, a realistic timeline and a cost range, and you talk to the engineer who would design the system.

Book a free scoping call Send project details

Written scoping summaryyours after the call

1Recommended approachRAG with hybrid retrieval, or classical ML, or plain software. Including the option of not building.
2What the system ownsThe steps it resolves alone, and the steps that stay with a person.
3First metric and baselineThe number it has to beat, measured on your own data before any build.
4Timeline and cost rangePhased, with the first scored result inside 14 days.

Written by the engineer who would build itFree, no obligation, yours to take to any vendor

Registered IT services firm · MSA and NDA on request · USD invoicing · WhatsApp +1 501 420 2439