Hire Expert ML Developers — PyTorch, TensorFlow, MLOps & Production ML
Hire pre-vetted ML developers specialising in supervised learning, deep learning (PyTorch, TensorFlow), computer vision, NLP, time-series forecasting, recommendation systems, MLOps, and production ML deployment. Dedicated, part-time, or fixed-scope. Start in 3–5 business days.
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ML Skills & Expertise
Supervised learning, deep learning, computer vision, NLP, time-series forecasting, recommendation systems, anomaly detection, MLOps infrastructure, edge ML, and ML architecture consulting.
Supervised Learning & Classical ML
Production supervised learning systems — classification (XGBoost, LightGBM, Random Forest, Logistic Regression, SVM) for fraud detection, churn prediction, credit scoring, and lead qualification; regression (gradient boosting, neural networks) for demand forecasting, price prediction, and propensity scoring; feature engineering pipelines with scikit-learn Pipeline and ColumnTransformer; hyperparameter optimisation (Optuna, Ray Tune, GridSearchCV); and model calibration for well-calibrated probability outputs.
Deep Learning (PyTorch & TensorFlow)
Custom neural network architectures and deep learning model development — PyTorch nn.Module for custom architectures, training loops, and research implementations; TensorFlow/Keras for sequential APIs and production deployment; transfer learning and fine-tuning pre-trained models (ImageNet, BERT, etc.); custom loss functions for domain-specific objectives; mixed precision training (FP16) for GPU efficiency; and distributed training (PyTorch DistributedDataParallel, Horovod) for large-scale model training.
Computer Vision Systems
Production computer vision — image classification (ResNet, EfficientNet, ConvNeXt, ViT); object detection (YOLO v8/v9, Detectron2, DETR, Faster R-CNN); image segmentation (DeepLab, Mask R-CNN, SAM); OCR pipelines (Tesseract, EasyOCR, PaddleOCR, custom deep learning OCR); video analysis (action recognition, multi-object tracking, anomaly detection); and industrial quality control vision systems (defect detection on imbalanced datasets using augmentation, focal loss, and few-shot techniques).
NLP & Text Classification
Natural Language Processing with Transformers — text classification (BERT, RoBERTa, DistilBERT fine-tuned on domain-specific corpora); named entity recognition (NER); sentiment analysis; topic modelling (LDA, NMF, BERTopic); text summarisation (T5, BART, Pegasus); question answering; document similarity and semantic search (sentence-transformers, FAISS); and custom NLP pipelines for document processing, contract analysis, customer support classification, and content moderation.
Time-Series Forecasting
Time-series forecasting systems — statistical methods (ARIMA, SARIMA, exponential smoothing) for well-behaved series; Prophet for seasonality-aware business forecasting; LSTM and GRU for sequence learning; Temporal Fusion Transformer (TFT) for multi-horizon forecasting with exogenous variables; multi-variate forecasting; anomaly detection in time series (LSTM autoencoder, STL decomposition + residual analysis); and production forecasting pipelines with automated retraining schedules.
Recommendation Systems
Production recommendation systems — collaborative filtering (matrix factorisation, ALS, BPR); content-based recommendations (TF-IDF, embedding similarity); two-tower neural networks (user and item embedding towers); sequential recommendation (BERT4Rec, SASRec); multi-stage retrieval + ranking (FAISS for ANN retrieval, LightGBM or neural ranker); real-time recommendation serving (sub-10ms latency with pre-computed embeddings + FAISS index); and A/B testing framework for recommendation algorithm comparison.
ML Tools & Frameworks
PyTorch, TensorFlow/Keras, scikit-learn, XGBoost, Hugging Face Transformers, YOLO v8, Detectron2, MLflow, Weights & Biases, Kubeflow, FastAPI, Triton, ONNX, FAISS, SageMaker, Vertex AI, and Evidently AI.
How to Hire an ML Developer
Full-time dedicated ML engineer, part-time specialist, or fixed-scope ML project — structured for your ML maturity and engineering stage.
- ML platform or product under active development (recommendation engine, CV system, forecasting service)
- Teams building MLOps infrastructure alongside data scientists
- Products that need a specialist ML engineer beyond what a data scientist alone can deliver
- Replacing or extending an internal ML team during a growth phase
Available within 3–5 business days
- Existing ML models needing performance improvement or MLOps infrastructure
- Data science teams who need an ML engineer for productionisation
- Adding a new ML feature to an existing product on a defined timeline
- MLOps audit and infrastructure improvement project
Available within 2–3 business days
- Fraud or churn prediction classifier (train, evaluate, deploy API)
- Demand forecasting system (historical data to production Prophet/LSTM)
- Computer vision quality inspection system (defect detection)
- NLP text classifier or sentiment analyser (fine-tune + deploy)
Typical 4–14 week engagements
Our ML Developer Hiring Process
From ML problem definition to first production model in 3–5 business days — domain-specific vetting, data assessment, rigorous evaluation, and MLOps-ready deployment.
Tell us about your ML problem — the prediction task (classification, regression, ranking, anomaly detection, computer vision, NLP), available data (labelled data volume, quality, feature richness), current baseline (rule-based system, previous model), desired output (REST API, batch scoring, edge deployment), and success metrics (accuracy, F1, latency, business KPI). Good ML starts with problem framing — the clearer the problem is defined, the better we match and scope.
Within 24 business hours we send 2–3 ML developer profiles matched to your domain and ML task — with specific experience in your problem type (fraud, CV, NLP, forecasting, recommendations), frameworks used (PyTorch, TensorFlow, sklearn, XGBoost), and MLOps experience (MLflow, Kubeflow, SageMaker). Not generic "ML developer" profiles — specialists matched to your problem.
Interview candidates on your ML problem. Ask them to describe their approach to training a fraud detection model on 0.1% positive rate data, design the feature engineering pipeline for your domain, or explain how they would set up drift detection for a model in production. Test real ML reasoning — data splitting strategy, evaluation metric selection, overfitting diagnosis, and production monitoring.
Before model training begins, our ML developer conducts a data assessment — reviewing data quality (missing values, label noise, train/test leakage risk), data volume sufficiency for the desired model complexity, feature engineering opportunities, and baseline performance using simple models (always start simple). We document the ML problem specification: input features, target variable, evaluation metrics, acceptable latency, and deployment environment.
Iterative model development with experiment tracking (MLflow or W&B), baseline then progressively complex models, rigorous cross-validation (stratified, time-based for time-series), model evaluation against business-relevant metrics (not just accuracy), feature importance analysis, and error analysis. Productionisation: model export (ONNX, TorchScript), REST API serving (FastAPI), Docker containerisation, and deployment to your infrastructure (AWS, GCP, Azure, or on-premise).
Post-deployment: model performance monitoring (prediction latency, throughput, and accuracy metrics on production data), data drift detection (input feature distribution shifts that degrade model performance), concept drift detection (target variable distribution change), automated retraining triggers, and model versioning. Ongoing support for model improvement, new feature engineering, dataset expansion, and model version upgrades.
What Our Clients Say
Data teams, engineering directors, and VP Data Science roles across the UK, AU, and US on hiring ML developers from 1Solutions.
We hired an ML developer from 1Solutions to build our customer churn prediction system — XGBoost model on 18 months of subscription data, engineered 40+ features, and deployed as a FastAPI service integrated with our CRM. Churn model precision at 20% recall: 71%. Customer success team uses daily scoring to prioritise outreach. Retention improved 14% in the first quarter.
1Solutions provided a computer vision ML developer for our manufacturing defect detection system — YOLOv8 trained on 3,200 labelled defect images, deployed on NVIDIA Jetson at the production line edge. False negative rate (missed defects) dropped to 0.8% from 12% with manual inspection. He built the labelling pipeline, handled the severe class imbalance, and wrote the edge deployment code himself.
Our fraud team needed a real-time transaction scoring model — 0.09% fraud rate, 200ms latency requirement, 50K transactions/day. 1Solutions sent an ML developer who trained an LightGBM model with 800+ engineered features, deployed on Triton Inference Server with GPU batching, and hit 4ms p99 latency at our load. Fraud catch rate at 1% FPR increased from 52% to 78%.
Why Hire ML Developers From 1Solutions
Production ML (not just notebooks), rigorous evaluation methodology, domain-specific problem framing, MLOps from the start, latency optimisation, imbalanced data expertise, clear model documentation, and transparent IP.
Production ML, Not Just Notebooks
An ML model in a Jupyter notebook is not a product. Our ML developers build the full pipeline: clean data ingestion, reproducible feature engineering, tracked experiments, model versioning, REST serving APIs, Docker deployment, and monitoring. Production-ready ML from day one.
Rigorous Evaluation Methodology
Model evaluation requires discipline: stratified k-fold or time-based cross-validation, held-out test sets never used during development, calibrated probability outputs for decision-making, business-relevant metrics (precision-recall, not just accuracy), and error analysis to understand what the model gets wrong and why.
Domain-Specific Problem Framing
The best ML system for fraud detection is different from the best for demand forecasting, computer vision inspection, or NLP classification. Our ML developers match their approach to the domain: class imbalance handling for fraud, seasonality for time series, data augmentation for CV, transfer learning for NLP.
MLOps from the Start
Model drift is inevitable. Our ML developers set up experiment tracking, model versioning, and drift monitoring before the first model goes to production — not after a model silently degrades for 3 months. MLflow, Evidently AI, and alerting are part of every production ML deployment.
Performance Optimisation for Latency
Real-time ML has latency requirements. Our ML developers optimise models for serving: INT8 quantisation, TensorRT GPU acceleration, ONNX for cross-framework deployment, batching strategies for GPU utilisation, pre-computation for recommendation caching, and FAISS approximate nearest neighbour for sub-5ms retrieval.
Imbalanced Data Expertise
Real ML problems have imbalanced data — fraud (0.1%), defects (1–2%), rare medical events. Our developers handle imbalance correctly: SMOTE, class weighting, threshold tuning against the actual business objective, and evaluation with precision-recall AUC (not misleading ROC AUC on imbalanced datasets).
Clear Model Documentation
Every model has a Model Card — architecture, training data description, evaluation results across subgroups, known limitations, intended use cases, and performance characteristics under distribution shift. Documentation is a deliverable, not an afterthought.
Transparent Code and IP
All model training code, feature engineering pipelines, evaluation notebooks, and deployment code are yours — well-structured Python packages, documented, and not dependent on proprietary frameworks. You can retrain, modify, and extend without us.
Hire an ML Developer Today
Share your ML problem — prediction task, data volume and quality, deployment environment, and success metrics — and we will shortlist pre-vetted ML developers within 24 business hours matched to your domain and ML task type.
Shortlisted ML developers within 24 business hours
Domain-matched vetting — CV, NLP, forecasting, fraud, recommendations
Full-time, part-time, or fixed-scope — flexible engagement
Data assessment before model training begins
5-day rapid replacement guarantee
Tell Us Your ML Problem
Hiring ML Developers — FAQ
Common questions about hiring ML developers — PyTorch, TensorFlow, MLOps, computer vision, NLP, imbalanced data, and production deployment.
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