Specializations

AI & Machine Learning Engineer Recruitment

Building AI-powered products requires engineers who understand the technology behind them. LusuTech helps companies define and recruit for AI, machine learning, and AI-platform engineering roles.

Who This Helps

SaaS products

Adding LLM features customers rely on.

Prototype to production

Hardening a promising demo.

ML infrastructure

Standing up training, serving and monitoring.

Data foundations

Fixing the data an AI feature depends on.

Which AI Role Do You Need?

The title says little on its own. What the person builds, and what to look for, is what matters.

AI Engineer / Applied AI

Builds AI features into products: LLM integration, retrieval, evaluation. Look for features shipped to real users.

Generative AI / LLM Engineer

Builds RAG systems, agents and evaluation pipelines. Look for evaluation discipline, not just demos.

Machine Learning Engineer

Trains and deploys predictive models. Look for production deployment, not only notebooks.

MLOps Engineer

Owns pipelines, serving, monitoring and reproducibility. Look for reliability habits.

AI Platform Engineer

Builds shared infrastructure for AI teams. Look for platform engineering with ML context.

AI-focused Software Engineer

Builds the product software around AI capabilities. Look for strong software fundamentals.

Relevant Technical Skills

Skills employers may require for these roles. Every role is different, and no single candidate has all of them.

PythonPyTorch and ML frameworksLLM applications and APIsRetrieval-Augmented Generation (RAG)AI agent developmentModel evaluation and monitoringData pipelinesVector databasesCloud deploymentMLOps

Common Hiring Scenarios

Adding LLM features to a SaaS product

Retrieval, evaluation, cost and latency all matter as much as the model.

Taking a prototype to production

Monitoring, guardrails and reliability for real users.

Standing up ML infrastructure

Training, serving and model lifecycle for a growing team.

Fixing the data foundations

Often the real blocker. Data engineering may be the first hire.

Our Recruiting Approach

  • Discovery: what the AI system must do, and for whom
  • Role calibration: essential skills and what can be learned
  • Candidate research: evidence of shipped work, not keywords
  • Qualification: a person reviews each profile
  • Consent: candidates are introduced only with permission
  • Interview coordination through to your decision

Engagement Options

  • Direct hire for permanent AI and ML engineers.
  • Contract for defined AI projects, subject to engagement terms.

We focus on applied and production AI roles. We don't recruit for academic research positions.

AI Hiring Questions

Rarely, for applied roles. We'll discuss what the work actually requires.

If you're building on top of foundation models, an LLM or applied AI engineer. If you're training your own predictive models, an ML engineer.

Often, and strong data foundations matter. We'll discuss whether to hire for AI experience now or for fundamentals.

Related specializations:  Data Engineering  ·  Software Engineering

Need to Hire an AI Engineer?

Share the role or the problem it needs to solve. We'll reply by email to discuss it.