Verified on August 4, 2026.

How to Choose an LLM Integrator: Lessons from OpenAI, Claude, and AWS

For LLM implementation, don’t hire “the person who knows how to write prompts,” but rather an engineer-consultant responsible for the full lifecycle: identify the business problem, build a prototype, integrate it with the company’s data and systems, set up evals, ensure security, launch to production, and drive employee adoption. That is exactly the profile repeated in job postings from OpenAI, Anthropic—the creator of Claude—and Amazon Web Services (AWS).

In short: a strong LLM integrator combines four roles: software engineer, solutions architect, product thinker, and change consultant. Vibe coding is useful as a way to quickly test a hypothesis. But code generation speed does not replace architecture, testing, observability, security, or accountability for the business metric.

This article is for business owners, CEOs, CIOs/CTOs, and product leaders choosing a consultant, contractor, or internal implementation lead. It is not an agency ranking or a list of open jobs: we use the requirements from OpenAI, Anthropic, and AWS themselves as a public model of a mature candidate.

Who the creators of LLMs are really hiring
The 6P framework: six areas of responsibility
Can you hire a vibe coder?
100-point candidate scorecard
12 interview questions
What test assignment to give
What to include in the contract
Red flags
FAQ
Final candidate profile

Who the creators of LLMs are really hiring

We reviewed nine customer-facing roles: three at OpenAI, three at Anthropic, and three at AWS. The titles differ—Forward Deployed Engineer, Applied AI Architect, Specialist Solutions Architect—but the work scope is almost identical.

Company	Public roles	What the person must deliver
OpenAI	Forward Deployed Engineer, Forward Deployed Software Engineer, AI Deployment Engineer	discovery, scope, full-stack build, production rollout, adoption, and measurable impact
Anthropic	Applied AI Architect for digital-native, enterprise tech, and industries	business discovery, Claude architecture, prototypes, evals, security, and deployment
AWS	GenAI Specialist SA, Applied AI Solutions Architect, Partner Solutions Architect	data readiness, RAG and agents, integrations, evals, production readiness, and repeatable patterns

In the Forward Deployed Engineer jobs in London OpenAI explicitly combines discovery, technical scope, design, development, and production rollout. Role success is measured not by the number of prototypes, but by production adoption, workflow impact, and feedback based on evals. It requires 5+ years of engineering or deployment experience and the ability to write production code in Python, JavaScript, or a comparable stack.

Forward Deployed Software Engineer is an even more engineering-heavy version. The candidate embeds with the customer team, builds full-stack solutions, prepares plans for both PoC and production, and writes code alongside the client’s engineers. OpenAI asks for 7+ years of full-stack experience and considers founding or early-stage engineer experience—having built a product from scratch—a plus.

In the AI Deployment Engineer, Large Enterprise role, the focus shifts toward enterprise adoption: define a GenAI roadmap, choose high-value use cases, and take them from prototype to industrial-scale implementation. This role shows why a technical person without the ability to speak with leadership is not enough either.

At Anthropic, Applied AI Architect for digital-native companies guides the client from technical discovery through evaluation to deployment, designs Claude integration, and helps create an evaluation framework. The formal minimum is 5+ years in customer-facing technical roles. The application includes a telling filter: whether the candidate has written Python or TypeScript in the last 12 months and built a working LLM prototype for a customer.

Applied AI Architect, Enterprise Tech and Applied AI Architect, Industries repeat the same profile: enterprise architecture, Python, LLM tools, evals, and communication from the C-level to engineers. In other words, a “presenter without hands” and a “coder without business context” are equally incomplete.

AWS adds a data and operations layer. Sr. Applied AI Solutions Architect for Amazon Connect assesses CRM readiness, knowledge bases, and backend systems, builds RAG, Lambda/API integrations, MCP servers, and agent-to-agent workflows, and runs evals for accuracy, latency, and customer satisfaction.

GenAI Specialist Solutions Architect for EU North must provide recommendations on security, cost, performance, reliability, and operational efficiency. And the Partner Solutions Architect turns one-off solutions into reference architectures, blueprints, and repeatable integration patterns.

One conclusion follows from these nine roles: LLM implementation is product and organizational engineering, not just API integration.

The 6P framework: six areas of responsibility

To avoid judging a candidate by the polish of their presentation, use the 6P framework. Each letter corresponds to an outcome that can be verified.

Area	What the integrator must do	What to ask for as proof
Problem	find an expensive, repeatable business process	process map, baseline, and metric owner
Prototype	quickly validate a technical hypothesis	working prototype on real examples
Platform	integrate the solution with data and systems	API architecture, RAG, IAM, logs, and fault tolerance
Proof	prove quality and economics	eval set, acceptance criteria, cost per successful task
Production	launch and operate	rollout, observability, runbook, rollback, and SLA/SLO
People	achieve adoption	training, process change, feedback loop, and adoption metric

1. Problem: starts with the process, not the model

A strong consultant first asks: who does the work, how many cases come through per week, where the bottleneck is, how much the error costs, and what decision remains with the human. A weak one starts with, “let’s put an agent on Claude or GPT.”

Before the prototype, at least four numbers should appear: the current operation duration, execution cost, error frequency, and case volume. Without a baseline, it is impossible to prove impact. We covered this sequence in more detail in the article “Introducing AI into Business Processes: Where to Start”.

2. Prototype: can independently build a working proof of concept

An integrator does not have to build an entire enterprise platform alone. But they must be able to personally assemble a narrow PoC in Python or TypeScript, connect the model API, data, and one real tool, and then explain every architectural decision.

In the prototype, you are not testing a “wow” answer, but the full path: input data → model call → tool use → validation → result write-back → error handling. If the candidate can only show a chat in a no-code builder, you have not yet tested integration competence.

3. Platform: understands data, security, and the existing IT environment

LLMs almost never work in a vacuum. They need documents, CRM, ERP, access rights, APIs, and an activity log. That is why the candidate should discuss:

data quality, freshness, and data owners;
RAG, indexing, and knowledge base update rules;
identity and access management, tenant isolation, and secrets;
protection against prompt injection and dangerous tool calls;
timeouts, retries, idempotency, and graceful degradation;
logs, tracing, cost, and model versions.

AWS in particular checks data readiness very clearly before launching agents. That is an important hiring signal: if a consultant promises quality without a data audit, they are selling a model, not a system.

4. Proof: builds evals before scaling

Evals are a set of real tasks with criteria for the correct result. For support, this can include answer accuracy, the share of correct policy citations, success rate of an action in CRM, and escalation frequency to a human. For document processing, it can include field completeness, critical errors, and review time.

A minimum pilot should include 50–100 real cases from your process, including rare and unsafe scenarios. The sample size is a practical AI dawn estimate, not a universal standard. It is important to define in advance what counts as a successful result; otherwise, the team will start cherry-picking pretty examples after the test.

The main economic metric is cost per successfully completed task, not the price of a million tokens. It includes tokens, infrastructure, retries, manual review, support, and the cost of errors.

5. Production: can launch, observe, and roll back

Production starts where there are real users, permissions, incidents, and accountability. The candidate should bring a phased rollout plan: shadow mode, limited group, traffic percentage, stop criteria, fallback, and rollback.

Ask them to show a runbook for at least one previous AI service: how quality, latency, costs, tool errors, and model behavior changes were tracked. If, after delivery, only the prompt and an API link remain, operations have been handed over to you.

6. People: owns adoption, not just deploy

A technically correct agent does not create impact if employees work around it or manually recheck everything. OpenAI explicitly uses production adoption and workflow impact as success criteria for the Forward Deployed Engineer.

That is why a project needs a business process owner, training, a feedback interface, escalation rules, and a weekly review of pilot failures. Implementation does not end at deployment; it ends when the new process is consistently used and delivers measurable results.

Can you hire a vibe coder

Yes — for a prototype with limited risk. No — as the sole owner of a critical implementation, if they have not demonstrated the other five 6P areas.

Here, a vibe coder means someone who quickly assembles an application using AI coding tools. It is a way of working, not a profession and not an automatic negative. OpenAI itself values iteration speed and early engineer experience. Anthropic directly asks candidates about using AI coding assistants and agentic developer tools.

The difference comes down to accountability:

Appropriate	Risky
internal prototype without sensitive data	autonomous access to payments, personal data, or production changes
landing page, mockup, demo interface	code without review, tests, logging, or an owner
testing one workflow on a copy of the data	promise to replace the process after three successful demos
working under the guidance of a strong architect	dependence on one model, one account, and hidden prompts

Hire not based on whether someone writes code by hand or with AI, but on their ability to explain the system, test it, and take responsibility after launch.

Candidate scorecard out of 100

Give each interview participant one table. Evaluate only evidence: an artifact, code, a metric, a diagram, or an analysis of a real incident.

Competency	Points	What counts as strong evidence
Business discovery and prioritization	20	baseline, unit economics, choosing a use case by impact and risk
Hands-on engineering	20	production code, API, Python/TypeScript, databases, tests
Architecture, data, and security	20	RAG/data pipelines, IAM, threat model, integrations, and fault tolerance
Evals and outcome measurement	15	set of real cases, automated and expert evaluation, regression gate
Production delivery	15	CI/CD, observability, rollout, rollback, runbook, and cost optimization
Communication and adoption	10	working with C-level leaders, engineers, and the process owner, user training
Total	100	recommended passing score — 75, with no zero in any row

The 75 threshold is Estimated AI dawn recommendation, not an industry standard. For high-risk processes, raise the threshold for architecture/security and production delivery. A candidate with 95 points for sales and demos, but zero for evals, does not pass.

12 Questions to Ask in an Interview

How would you choose the first use case? A strong answer starts with volume, cost of error, baseline, data availability, and process owner.
Show us the system you took from PoC to production. We need code or a diagram, timelines, failures, metrics, and the current status.
How did you build the evaluation set? Look for real cases, edge cases, pre-test criteria, and a regression run.
How did you calculate the business impact? The answer should include manual labor, infrastructure, verification, and errors.
When is RAG not needed? A mature candidate does not automatically add a vector database and can choose search, SQL, tools, or plain context.
How do you secure an agent with access to actions? Look for least privilege, allowlist, confirmation for risky actions, audit, and isolation.
What happens if the model or CRM becomes unavailable? You need timeout, retry policy, queue, fallback, and a clear user-facing status.
How will you detect degradation after changing the model? Versioning, a frozen eval set, canary rollout, and metric comparisons.
How will you control costs? Model routing, caching, compact context, budgets, alerts, and cost per successful task.
How will you hand the solution off to our team? Repository, documentation, IaC, runbook, training, and no contractor secrets.
Tell us about a failed AI project. A strong candidate names their own mistakes and process changes.
How will you measure adoption after one month? You need active users, share of target flow, operation time, override rate, and reasons for refusal.

Do not accept generic answers. After each one, ask: “Show me the artifact” or “What number changed?”

What take-home assignment should you give

A short paid pilot is better than a free presentation. Give the candidate one narrow process, anonymized data, and 5–10 business days.

Input conditions

20–30 examples for discovery and another closed set for validation;
one API or a test CRM/ERP environment;
limits on data, cost, and response time;
an employee — the process owner — for two working sessions.

Required deliverables

A one-page problem map: baseline, user, risk, and target metric.
A working end-to-end prototype, not a series of screenshots.
An architecture diagram, data flow, and access control model.
An eval set and a results table on closed examples.
An estimate of total cost and a list of unknowns.
A production rollout plan, monitoring, rollback, and handoff to the team.
A short postmortem: what did not work and why.

Do not ask for a finished product for free. The goal of the test is to assess thinking style, engineering depth, and honesty about uncertainty.

What to include in the contract

The contract should buy not “a bot,” but verifiable stage deliverables.

The deliverables belong to the client: code, prompts, evals, diagrams, documentation, IaC, and configuration logs.
Acceptance criteria are defined in advance: quality, latency, cost, security, and share of successful tasks.
There are stop/go gates: discovery, PoC, pilot, production; each stage can be stopped without paying for imaginary scale.
There is no hidden dependency: keys, accounts, and infrastructure are set up under the client’s ownership; transfer between models has been assessed.
Operations are defined: support, incidents, eval updates, change management, and knowledge transfer.
Data risks are documented: data classes, storage regions, retention, subprocessors, and deletion procedures.

If the company does not yet have an internal technical owner, add an independent architecture review before production. The contractor alone should not be the only person confirming the quality of their own work.

Red flags

starts the meeting by choosing a model without understanding the process or baseline;
promises a percentage of savings before accessing the data;
shows only perfectly tailored demos and refuses a closed test;
treats prompt engineering as a sufficient architecture;
cannot explain evals, regression testing, and human escalation;
asks for production access before a threat model and a list of allowed actions;
does not discuss latency, retry cost, logs, or external API failures;
stores code, prompts, or keys only in their own accounts;
promises a fully autonomous agent where an error has legal or financial significance;
cannot name a single failure or a single decision that had to be rolled back.

An additional risk is a “large team” where a strong architect attends the meeting, but the post-sale work is done by an unknown junior. Document the specific technical lead, their level of involvement, and the right to approve a replacement.

FAQ

What is the role called for an LLM implementation specialist?

Market titles include LLM integrator, AI solutions architect, applied AI architect, AI deployment engineer, and forward deployed engineer. The title is secondary: verify whether the person owns discovery, code, architecture, evals, production, and adoption.

How is an LLM integrator different from a regular consultant?

A regular consultant may finish with recommendations. An LLM integrator should be able to build the technical validation themselves, integrate it into the IT environment, define quality criteria, and carry the solution into operations together with the client team.

Does the candidate need machine learning experience?

Deep experience training foundation models is not always necessary. What matters is understanding LLM behavior, evals, data, RAG, and agentic workflows, plus a strong software/cloud engineering foundation. Anthropic allows familiarity with LLM frameworks or a background in ML/data science; OpenAI places more emphasis on production engineering.

How many years of experience should you look for?

In the roles reviewed, OpenAI and Anthropic often require at least 5+ years; for a more engineering-heavy OpenAI role, 7+ years; and for an AWS Partner Solutions Architect, 8+ years in technical domains. For business, the number itself matters less than a proven cycle from discovery to production.

Should the integrator know GPT, Claude, or AWS Bedrock specifically?

They should know the chosen stack for the first launch in depth, but not build an architecture that unnecessarily locks the company into one provider. Ask them to explain which parts are portable and which depend on a specific model or cloud.

How do you know a pilot is successful?

Before launch, define the baseline and criteria: the share of tasks completed successfully, critical errors, time, cost, human review, and adoption. Success is an improved workflow on a closed test set and with real users, not a positive reaction to the demo.

Ideal candidate profile

Look for a senior LLM integrator at the level of a Forward Deployed Engineer / Applied AI Architect with the following profile:

5–10 years in software engineering, solutions architecture, ML/data engineering, or technical consulting; for an independent owner of enterprise implementations, 7+ years is preferred;
personally writes and reviews production code in Python or TypeScript, works with APIs, SQL, queues, cloud, and CI/CD;
can lead business discovery, establish the baseline, and choose a use case based on impact, risk, and data availability;
designs RAG, agentic workflows, tool use, MCP/A2A, and enterprise integrations only where they are justified;
understands data readiness, IAM, privacy, prompt injection, guardrails, audit logs, and human approval for risky actions;
builds evals on real-world cases, including edge cases, and connects model quality to business metrics;
has taken at least one solution from PoC to production, and can show rollout, observability, incident response, rollback, and cost optimization;
speaks equally clearly with C-level leaders, process owners, security, and engineers, without hiding trade-offs or unknowns;
is responsible for production adoption and knowledge transfer, rather than disappearing after the demo;
can name their failures, show artifacts from past work, and pass a paid test on your closed case.

Hiring formula: strong engineer + solutions architect + product thinker + change consultant. Vibe coding can speed up their work, but it is not a substitute for any of these four parts. The main KPI is not the number of AI demos, but the share of tasks completed reliably, measurable impact, and the speed of a safe transition from prototype to a working process.

How to Choose an LLM Integrator: Key Lessons