Hire pre-vetted ML Developers

Not every problem needs a large language model. For tabular data, time series, structured prediction, and anomaly detection, a properly engineered XGBoost or LightGBM model still outperforms everything else. The issue is finding someone who actually knows that, and knows how to ship it.

Most "ML engineers" on the market today jumped straight to LLMs and skipped the fundamentals. Feature engineering, proper validation, monitoring for drift after deployment. The ML engineers on Cortance didn't skip anything. They have years of production experience with classical machine learning, and their profiles prove it with real project histories, not self-reported skills.

Cortance has made 150+ successful placements across engineering and AI roles, with a 94% retention rate and 200% year-over-year growth since launch. The ML engineers in this network average 6 years of commercial experience. That number reflects something specific in this field: engineers who've watched models degrade in production, diagnosed why, and fixed it — not once, but across multiple clients and multiple domains.

What these ML engineers have built

Player behavior prediction in gaming: 38 months running churn, monetization, and segmentation models at scale for a major gaming company. Insurance reserve calculations from actuarial data. Ad pricing optimization that increased a client's advertising profit by 30%. Bearing fault detection for industrial maintenance. Aspect-based sentiment analysis and named entity recognition for biomedical literature using BERT and transformer models. These are production systems with measurable results, not Kaggle notebooks.

Each project listed on a Cortance profile was delivered for a real client in a production environment. Before a profile goes live, the work history is verified — not through interviews or self-assessment, but through documented project delivery. You see what was built, what stack was used, and what the outcome was.

The core stack is Python, scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow. For production, they work with Docker, CI/CD pipelines, DVC for data versioning, MLflow for experiment tracking, and model monitoring to catch drift before it becomes a problem. Many of these engineers also handle the data engineering side when needed: pulling from warehouses, working with Spark or Airflow, and building the pipelines that feed their models.

The most common issue we see when companies arrive at Cortance after a previous ML hire didn't hold up: the engineer knew how to train a model but had never owned it past the first deployment. Model drift, data distribution shifts, retraining pipelines — those problems appear at month three, not week one. Engineers with genuine production experience account for them in the original architecture.

Also on this team: autonomous systems and embedded AI

Worth mentioning separately because it's a niche strength. Several ML engineers on Cortance have built AI for autonomous UAV navigation in GPS-denied environments, target detection and tracking from drone cameras at 60 m/s, and satellite-based positioning using image matching. This work sits at the intersection of ML, control theory (Kalman filtering, PID controllers), and embedded deployment on Jetson and Raspberry Pi. If your project involves robotics, autonomous vehicles, or industrial automation, these profiles are worth checking.

Embedded ML has different constraints than cloud deployment — memory budgets, inference latency targets, thermal limits, and hardware-specific optimization pipelines. Engineers who've shipped models on Jetson and Raspberry Pi understand those constraints before the first line of code, not after the hardware arrives.

How hiring works

Submit your requirements through the platform, fill a short questionnaire, or reach out to a hiring manager directly. Cortance's AI-powered matching system scores candidates against your domain, stack, deployment target, and seniority level — and delivers a curated shortlist of matched ML engineers within 30 minutes. Most clients complete the hire in 2 days. Cortance handles contracts, payroll, and onboarding. If a match doesn't work out within the first 2 weeks, a replacement is arranged at no extra cost.

Remote, flexible, transparent

All ML engineers on Cortance are remote and based in Europe (Ukraine, Portugal, Spain, Georgia). Available for freelance, contract, or dedicated long-term positions. Hourly rates are visible on each profile. You can compare specialists side by side, review their full project histories, and reach out directly. No agency middleman, no markup surprises.

If you need to hire a machine learning engineer with real production experience, not someone who retrained a tutorial model once, start with the profiles at the top of the page.

Frequently Asked Questions

1. Do you have ML engineers with embedded or edge deployment experience? Yes. Several engineers have deployed models on Raspberry Pi, NVIDIA Jetson, and Intel Neural Compute Stick, including full optimization with TensorRT and quantization for real-time inference.
2. What industries do your ML engineers cover? The strongest concentrations are in fintech, agritech, gaming, healthcare, and defense. But the team has also delivered in insurance, e-commerce, sports analytics, and industrial automation. Check individual profiles for specifics.
3. Can I hire an ML engineer who also handles the data engineering layer? Yes. Several engineers on the platform cover both sides: building the models and building the pipelines that feed them. If your project doesn't justify separate data engineering and ML engineering roles, check individual profiles for Spark, Airflow, and warehouse experience.
4. Do your ML engineers work with LLMs as well as classical ML? Some do. Several engineers on the platform have added LLM, RAG, and agent engineering to a foundation of classical machine learning. Their profiles show exact years per technology, so you can judge the depth of each area independently. If your project is primarily LLM-based, the GenAI Engineer profiles are a better starting point.
5. What if I need an ML engineer for a short evaluation or scoping engagement? Short consulting engagements are available. For ML projects specifically, a scoping engagement often helps define realistic accuracy targets, identify the right model family for your data type, and surface data quality issues before full development begins — which typically saves time and budget downstream.