AI Engineers who automate business processes and keep them running

Most teams need AI engineering first. AI engineering fits a team turning an existing foundation model into a product feature, often within weeks. MLOps fits a team that trains its own models or runs LLM traffic at a scale where reliability, cost and drift matter more than feature speed. An MLOps hire usually pays off once a model is live and its running costs or error rates need someone's full attention.

The core shift is from deterministic control flow to probabilistic components. Traditional software guarantees that the same input produces the same output every time, which is why unit tests with fixed pass/fail assertions work. AI systems built on trained models can return different outputs for identical inputs depending on sampling settings, context, or model version, so the architecture has to plan for that variability instead of assuming it away.

Start with a workflow automation when the steps are known in advance, and build an AI agent only when the next step depends on reading the situation. A scripted workflow with one model call is cheaper to run and easier to test. An agent earns its cost when inputs vary too much for fixed rules, and even then it needs defined schemas, retry limits and a person approving risky actions.

Buy an off-the-shelf AI tool when the task is generic, such as meeting notes or email drafts, and build a custom AI agent when it has to work inside your own data, rules and systems. Ready-made tools rarely reach an internal CRM, ERP or approval flow. A custom agent costs more upfront, but you control which data it sees, which actions it may take and which model it runs on.

AI engineers stop an agent from acting wrongly by validating every output against a schema and routing low-confidence decisions to a person. The agent gets an allow-list of tools it may call, limits on retries, and a fallback for states it cannot resolve. Actions that change data or reach customers wait in an approval queue, and an audit log records each recommendation and who approved it.

Prompt engineering is enough for a demo, but a system that answers from company documents needs an AI engineer with production RAG experience. The hard parts are chunking, metadata filters, keeping each client's data apart and showing where an answer came from. A good sign in a candidate's history is a shipped retrieval system, such as the contract audit tool built by Bohdan Bo. in Cortance's network, which links each finding to the exact clause.

Hiring an AI engineer through Cortance typically costs $39 to $52 an hour. Where a request falls in that range depends on seniority and on how much of the work is production RAG, agent orchestration or MLOps rather than a single model API integration. Once you confirm a match, the engineer can start within 2 days.

Usually, yes, for a single specialized role or a project with a defined scope, since hiring remotely avoids the overhead of recruiting, benefits, and idle time between projects that an in-house team carries. Building a full in-house AI team tends to make more financial sense only once AI work is a permanent, ongoing part of the product rather than a discrete initiative.

An AI engineer from Cortance's network can start within 2 days of a confirmed match. To speed up the match, send the problem rather than a job title: the process to automate, where its data lives and what the system may do without a person. That helps decide early whether the work needs an AI engineer, a GenAI specialist or a Machine Learning specialist.