Hire AI engineers for one production system, not a model demo.
RaftLabs provides AI engineers for teams building LLM, RAG, agent, document, image, or voice features inside real software. The engineer joins an existing product and engineering process with a defined workflow, evaluation set, data boundary, release authority, monitoring plan, and handover.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Evidence and scope
4 weeks
First term
One engineer or lean team with a bounded AI workflow.
$6K-$6.5K
Planning rate
Public per-person monthly range before role matching.
From $9.5K
Fixed alternative
A proof of concept with success criteria agreed first.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
01
Is an AI prototype waiting on evaluation, permissions, integration, or production engineering before users can trust it?
02
Does the team have model experiments but no owner for cost, latency, failure handling, and release quality?
Plain answer
Hiring AI engineers adds production capacity for LLM, RAG, agent, document, image, or voice systems. RaftLabs engineers join an existing team with a defined workflow, evaluation set, data boundary, monitoring, and handover. Planning rates are $6,000 to $6,500 per person monthly, with an initial four-week term.
The prototype answers well until the document, user, or model changes.
The demo used a small prompt set and one developer's account. Production brings permissions, missing context, tool failures, model updates, cost limits, and users who ask questions nobody rehearsed.
An AI engineer closes that gap only when the workflow and quality bar are explicit. Model choice matters, but evaluation, system behaviour, and operating ownership decide whether the feature lasts.
Relevant AI delivery proof
12 weeks
to deliver the Perceptional AI interview platform
RaftLabs project record
30-40 seconds
documented Brux image-generation time
RaftLabs project record
20%
less clinical decision time reported for a patient-monitoring AI layer
Project-recorded outcome, not independently audited
The Perceptional, Brux, and patient-monitoring AI cases show production work around language, image, and decision-support systems. Their outcomes belong to those products and do not guarantee what one embedded engineer will deliver.
Embedded AI engineering fits when the client owns the product direction and needs production capacity around a bounded workflow.
Choose a fixed project when the result is stable, or a proof of concept when feasibility is still the main question.
A fit
01
A product or engineering lead can own priorities, data policy, and release decisions.
02
The team can name one AI workflow, its users, representative inputs, and the result it must produce.
03
The engineer can access the product, model environment, evaluation cases, and domain reviewers.
Not a fit
01
The brief is to add AI without a user job or production decision.
02
Nobody can approve data use, tool permissions, refusal behaviour, or human escalation.
03
The only success measure is that a demo looks impressive.
Ownership
What an embedded AI engineer can take on
01
LLM and product integration
Connect model calls to the product through structured inputs and outputs,
authentication, usage limits, fallbacks, version controls, and interfaces that
expose uncertainty appropriately.
02
Retrieval and permissions
Build ingestion, search, ranking, source display, refusal, and document-level
access for RAG systems. Retrieval and answer quality are tested separately so
failures remain diagnosable.
03
Agents and tool use
Define permitted tools, arguments, approval gates, budgets, retries, logs, and
human handoff. The system should never gain business authority merely because
the model can call an API.
04
Evaluation and operations
Turn representative cases into repeatable tests, inspect quality by failure
type, and monitor model version, latency, cost, tool errors, refusals, and
user feedback after release.
Should you hire an AI engineer or commission an AI project?
Embedded AI engineer vs fixed AI project
Embedded engineer
Fixed project
Best fit
An evolving AI roadmap inside an existing team
One stable workflow with acceptance criteria
Direction
Client owns day-to-day priorities
Delivery team owns the agreed project plan
Uncertainty
Managed across the ongoing backlog
Bounded through discovery or a proof of concept
Commercial model
Monthly per-person rate
Fixed price for the agreed phase
Handover
Continuous inside client systems
Formal at the end of the project
If the team cannot yet write representative evaluation cases, start there. More engineering capacity does not turn an untestable AI idea into a production requirement.
Onboarding
From AI workflow gap to production ownership
The first four weeks test role fit, evaluation discipline, product contribution, and handover.
Before start
01
Define the workflow and quality bar
Choose one user job, inputs, outputs, evaluation cases, permissions, failure
path, and production measure. Record the decisions that must stay with a
person.
Week 1
02
Match the engineering depth
Review product, model, retrieval, data, infrastructure, and domain needs.
Prepare access and a bounded first task that reveals the real system without
risking a critical workflow.
Weeks 1-4
03
Ship with evaluation
Work through the client's reviews and release path while testing quality,
safety, latency, cost, and human handoff. Keep prompts, configuration,
evaluation cases, and decisions versioned.
End of term
04
Review and hand over
Assess evaluation and operating evidence, update tests and runbooks, and
review the next-term need. Continue, change the boundary, adjust the role, or
close with ownership returned cleanly.
Where embedded AI engagements fail
The model is chosen before the workflow
A provider or model name is not a requirement. Define the user job, quality bar cost, latency, and data boundary before selecting the implementation.
Evaluation is a demo script
A few successful prompts hide coverage gaps. Use representative and difficult cases, label failure types, and rerun them when models or retrieval change.
Tool access becomes authority
Every tool needs permitted inputs, budgets, logs, approval rules, and a recovery path. Consequential work should preserve human control.
One model bill has no owner
Track cost by workflow and model version, then set limits and fallbacks. A useful feature must remain affordable at expected traffic.
Monthly model
Plan on $6,000 to $6,500 per AI engineer each month.
Start with one bounded workflow and an evaluation set, or choose a fixed proof of concept when feasibility is the main uncertainty.
A fixed-scope AI proof of concept starts at $9,500. Production projects are priced after workflow, data, evaluation, integration, and operating boundaries are agreed.
Starting investment
$6K-$6.5K per person monthly
A lean team starts around $12,000 to $15,000 per month. The initial term is four weeks; role mix, allocation, and specialist depth set the final rate.
Evaluation belongs to the product
Prompts, configuration, representative cases, results, and known failure modes
stay in the client's repository or agreed systems.
No automatic model commitment
The role match follows the workflow and architecture. A specific provider,
model, agent framework, or vector store is not imposed before the evidence
supports it.
Choose one production boundary: a RAG answer flow, agent workflow, document pipeline, voice interaction, image feature, or shared evaluation and monitoring layer. The engineer can contribute across the product, but a named user job and quality bar keep the engagement from becoming open-ended experimentation.
An AI engineer often assembles and operates systems around foundation models: prompts, retrieval, tools, structured output, safeguards, evaluation, product integration, and cost. An ML developer is a better fit when the core work is data preparation, feature engineering, model training, validation, serving, and drift. Some people cover both; the role should follow the workload.
Prepare the product repository, development environment, model and cloud accounts, representative inputs, approved data handling rules, evaluation examples, and the people who own the workflow. Sensitive data access should be minimal. If evaluation cases do not exist, building them is the first deliverable.
Use a fixed project when one workflow and acceptance boundary can be agreed before delivery. Embedded capacity fits an evolving roadmap where the client already owns product direction, architecture, and release decisions. A paid proof of concept is safer when the main uncertainty is whether the approach works at all.
Use $6,000 to $6,500 per person per month for planning, consistent with RaftLabs' dedicated-team model. A lean team starts around $12,000 to $15,000 monthly. Role mix, allocation, duration, and specialist depth set the final rate. A separate fixed-scope AI proof of concept starts at $9,500.
Work with us
Show us the AI prototype that cannot cross into production.
Bring one user workflow, representative inputs, current evaluation, and system boundary. We will recommend an embedded engineer, a team, or a fixed project.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.