Hire AI engineers who ship LLM systems to production, not demos.

An AI engineer builds production systems on top of foundation models: retrieval pipelines, agents, tool-calling layers, output schemas, evaluation harnesses and the monitoring around all of it. The job is less about training models from scratch and more about wiring LLMs into your product so they behave reliably, stay within cost and latency budgets, and fail safely. In practice they own the prompt and context layer, the eval suite that proves the system works, and the integration glue between the model and your existing services. At Resourcifi this work runs under our Production-First AI method, so evaluation and observability are built in from day one rather than bolted on later.

Think of three lanes. A data scientist frames the problem, runs experiments and proves there is lift before anyone commits headcount, mostly working in notebooks and statistics. An ML engineer takes a model and makes it a reliable production service: training pipelines, feature stores, serving infrastructure and retraining loops, usually for structured-output models you own. An AI engineer works one layer up, composing LLMs and agents into product features, owning prompts, retrieval, tool use and the evals that keep generative output trustworthy. They overlap, but the buying decision usually comes down to whether you are validating an idea, productionizing a custom model, or shipping a feature on top of foundation models.

Solid software engineering first, because most of the job is integration, not research: Python, typed APIs, async and clean service boundaries. On the AI side, expect fluency with the major model providers and SDKs, retrieval and vector stores, orchestration and agent frameworks, structured-output and function-calling patterns, and a real discipline around evaluation rather than eyeballing outputs. They should also be comfortable with observability and cost or latency tuning, since a model that works in a demo can quietly blow your budget in production. A senior engineer can also tell you when not to use an LLM, which is often the more valuable judgment.

Two common shapes: a dedicated engineer or pod embedded with your team on a per-engineer, per-month basis, or a scoped project priced against a defined deliverable. Dedicated works best when the roadmap is open-ended and you want capacity that scales up or down; project pricing works when the outcome is well defined and you want a fixed scope. We use a global delivery model, and rates are typically about 70% below comparable onshore rates. We can walk you through which structure fits before you commit to anything.

Placement typically happens in under two weeks from the first call, because we are matching from in-house engineers rather than recruiting cold. Meaningful contribution usually starts in the first week of work through a pilot week, where the engineer gets into your codebase, ships something small and real, and you confirm the fit before fully embedding. The engagement moves through a discovery call, a skills match where we name the engineer, an interview, a pilot week, then embed, and you can scale from there. The honest ramp depends on how documented your systems are; a clean repo with a clear eval target gets to value faster than an undocumented one.

Yes, the default assumption is that they work in your repo, your CI, your cloud and your ticketing system, following your review and branching conventions rather than building in a silo. Embedding into an existing codebase is the normal case, not the exception, and the pilot week is partly there to prove they can navigate your stack before going deep. They adapt to your model providers and infrastructure choices rather than pushing a preferred toolset. If you have constraints like a regulated environment or a specific deployment target, raise them on the discovery call so we match accordingly.

All work product and IP are assigned to you under contract; what the engineer builds is yours. Engineers operate under signed NDAs and your access controls, working within the permissions you grant rather than copying data out. Resourcifi runs a documented, repeatable quality system, and we are comfortable working inside your security and compliance requirements, including limiting access to production data and using anonymized or synthetic data for development where appropriate. For sensitive environments we can scope data handling and access boundaries explicitly before work starts.

If you are building features on top of foundation models, chatbots, copilots, RAG over your documents, agents or LLM-driven workflows, you want an AI engineer. If you need to train, serve and maintain a custom model on your own data, fraud scoring, recommendations, forecasting or computer vision, you want an ML engineer. Many real systems need both, plus a data scientist upstream to confirm the approach is worth building. The cheapest mistake to avoid is hiring for model training when your actual problem is reliable integration of an existing model, or the reverse.

Ask them to design an evaluation suite for a feature, because strong engineers reach for evals instinctively and weak ones rely on vibes. Our engineers work to a three-layer eval standard: reference tests for known-good behaviour, adversarial tests for edge cases and prompt attacks, and regression tests so a prompt or model change does not silently break what worked. Probe how they reason about cost, latency, hallucination and graceful failure, and whether they can explain when an LLM is the wrong tool. Anyone who only talks about prompt cleverness and never about measurement is a risk in production.

Yes, this is common. Production Recovery is a recurring engagement type, work we see often, where a prototype demos well but cannot ship reliably. The usual culprits are no evaluation harness, no observability, brittle prompts, runaway cost or latency, and unhandled failure modes, which is exactly what our Production-First AI method is built to fix. We start by auditing the current state and standing up the eval and monitoring layer so progress becomes measurable, then stabilize and ship. With 95% repeat clients and a 4.9 rating on Clutch, sticking with a build until it is actually in production is the normal outcome, not the exception.

You approve the specific senior engineer before you sign, having met them on a technical interview and reviewed code samples, which removes most fit risk up front. The pilot week is the second safeguard: the engineer ships something small and real in your codebase before fully embedding, so a mismatch shows up in days rather than months. If the fit is still wrong, we move quickly to replace the engineer from the same vetted bench. Because we match from 200+ in-house experts rather than a marketplace, a replacement comes from the same vetted bench rather than a cold search.