DOIT helps startups and SMBs hire machine learning developers who understand how ML actually works on real products. Every developer your team interviews is already vetted on stack depth and collaboration fit.
Python
SQL
PyTorch
TensorFlow
scikit-learn
Polars
Pandas
NumPy
JAX
Hugging Face Transformers
OpenCV
YOLO11
SAM2
LangChain and LlamaIndex
vLLM
Ollama
OpenAI & Claude APIs
Open-weight LLMs
Image & speech
Spark
Kafka
Ray
Dask
Kubeflow
MLflow
Universal ML language
Deepest library ecosystem
Easy team onboarding
Direct data access
Strong feature pipelines
Standard data layer
Production-ready training
Research-friendly syntax
Strong community support
Enterprise-grade serving
Edge-ready deployment
Mature MLOps tooling
Fast prototyping
Classical ML standard
Clean Python API
Fast
Memory-efficient
Large-data ready
Efficient data handling
Preprocessing
Intuitive API
Fast computations
Matrix operations
TPU acceleration
High throughput
Functional design
Pre-trained model hub
Fine-tuning ready
Multimodal coverage
Real-time CV
Wide algorithm coverage
Mature CV standard
Real-time inference
Multi-task model
NMS-free architecture
Pixel-level precision
Video segmentation
Zero-shot ready
Faster RAG builds
Tool integration ready
Agent orchestration
High-throughput serving
Cost-efficient GPUs
PagedAttention engine
Local-first development
Quick prototyping
OpenAI-compatible API
Top-tier quality
Production-ready APIs
Multimodal coverage
Data privacy control
Lower inference cost
Custom fine-tuning
Current 2026 models
Production-grade quality
Multimodal coverage
Big data processing
Scalable ML
Distributed computing
Real-time
Scalable pipelines
Event-driven ML
Distributed training
GPU-optimized
Reinforcement learning ready
Python-native parallelism
Memory-efficient
Pandas-compatible API
Kubernetes-native
MLOps automation
Open-source flexibility
years of usage
Experiment tracking
Built-in model registry
Wide framework support
Computer vision systems
Computer vision engineers from the DOIT network specialize in object detection, image segmentation, video analytics, and vision-language models. They help businesses automate visual processes across security, healthcare, defense, and manufacturing workflows.
✔ Object detection and image segmentation
✔ OCR and intelligent document processing
✔ Video analytics for motion detection and tracking
✔ Vision-language search and multimodal retrieval
Predictive analytics and forecasting
Forecasting specialists from the DOIT network build demand forecasting, churn prediction, predictive maintenance, and risk modeling systems. They help businesses use historical data to anticipate revenue and attrition alongside operational risk patterns.
✔ Time-series forecasting for demand and revenue
✔ Customer churn and lifetime value modeling
✔ Predictive maintenance and equipment failure detection
✔ Risk modeling for finance and insurance
Recommendation systems
Recommendation systems engineers specialize in ranking models, behavioral personalization, content engines, and dynamic pricing systems. The DOIT network places specialists who help e-commerce and media businesses increase engagement and conversions through smarter content delivery.
✔ Product and content recommendation engines
✔ Personalized search and ranking algorithms
✔ Dynamic pricing and user behavior modeling
✔ Context-aware recommendation pipelines
NLP and language understanding
NLP engineers from the DOIT network work in text classification, semantic search, entity extraction, and language model fine-tuning. They help businesses extract value from unstructured text and customer conversations.
✔ Text classification and named entity recognition
✔ Semantic search and vector retrieval
✔ Multilingual translation and analysis
✔ Sentiment and intent detection
Generative AI applications
Generative AI engineers build LLM applications with fine-tuning, RAG pipelines, agentic workflows, and generative media integration. The DOIT network places specialists who turn open-weight and closed-API language models into real product features.
✔ Fine-tuned open-weight LLM applications
✔ Retrieval-augmented generation pipelines
✔ Agentic workflows with tool use and orchestration
✔ Generative image, audio, and video integration
MLOps and model deployment
MLOps engineers specialize in training pipelines, model registries, serving infrastructure, and monitoring. The DOIT network places specialists who keep ML models reliable across scaling, retraining, observability, and drift detection work.
✔ Training orchestration and scheduled retraining
✔ Model registries and version management
✔ Production monitoring and drift detection
✔ A/B testing and gradual rollout infrastructure
Anomaly detection
Anomaly detection specialists work across financial systems, cybersecurity events, IoT sensor streams, and operational signals. The DOIT network places engineers who help businesses spot fraud, breaches, operational outliers, and risk patterns as they happen.
✔ Financial fraud detection in real time
✔ Cybersecurity threat detection and triage
✔ IoT anomaly detection across sensor streams
✔ Production monitoring with anomaly alerting
Acceptance-to-hire rate for machine learning developers
Fluency standard for placed machine learning developers
ML developer locations
matched to your team's time zones
$55/hour
Kyiv, Ukraine
Full-time (remote)
Tech stack: Python, TensorFlow, PyTorch, OpenCV, NumPy, SciPy, Pandas, scikit-learn, YOLO11, U-Net, SORT, FAISS, FastAPI, Docker, Kubernetes
A machine learning engineer with 3 years of experience on computer vision systems for defense tech clients. Spent the last project building real-time object detection and tracking for drone applications, including aerial map generation from video and image retrieval systems that flag anomalies. Has worked closely with real-time UAV control teams, owning both the model side and the deployment handoff. Comfortable working solo on the CV side or as part of a small ML team.
Full-time (remote)
$58/hour
Warsaw, Poland
Part-time (20 hours/week)
Tech stack: Python, PyTorch, TensorFlow, JAX, Hugging Face Transformers, LangChain, LlamaIndex, vLLM, OpenAI API, FastAPI, AWS
An LLM engineer with two years of work on generative AI features for enterprise clients. Most recently built a retrieval-augmented chat system that lets a business team query its own knowledge base in natural language, including the evaluation framework that checks whether answers are correct. Has also shipped voice transcription pipelines and runs fine-tuned open-weight LLM inference for product deployments.
Part-time (20 hours/week)
$52/hour
Sofia, Bulgaria
Full-time
Tech stack: Python, MLflow, Kubeflow, Apache Airflow, Docker, Kubernetes, TensorFlow, OpenCV, scikit-learn
An MLOps engineer with 3.5 years of work on the production side of ML for telecom and finance clients. Spent the last engagement automating document-processing workflows that previously consumed hours of human work each day, with OCR plus ML classification handling the pipeline. Owns the deployment, monitoring, retraining, and observability side of ML systems so models stay reliable as the underlying data changes.
Full-time
$65/hour
Buenos Aires, Argentina
Full-time (remote)
Tech stack: Python, PyTorch, TensorFlow, NumPy, AWS, Microsoft Azure, scikit-learn, Hugging Face Transformers, LightGBM, MLflow, Weights & Biases
A senior ML engineer with 5 years of experience in predictive analytics and anomaly detection. Has shipped fraud detection systems for fintech clients, churn-prediction models for customer-success teams, NLP pipelines that pull structured data out of documents, and forecasting systems for operational planning. Comfortable owning the deployment side as well as the modeling, with MLflow and Weights & Biases as the working stack. Open to leading small ML teams or contributing as a senior individual contributor.
Full-time (remote)
$69/hour
Dallas, TX, United States
Full-time
Tech stack: Python, NumPy, Pandas, OpenCV, TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, SpaCy, MLflow, FastAPI, Docker, Git
A machine learning engineer with 4 years of commercial experience across NLP and computer vision projects. Most recent work was building a content-classification system for an EdTech platform that tags user-generated text with intent and topic categories then routes the output into a downstream personalization engine. Has deployment-side experience as well, shipping inference services through FastAPI and managing model versions with MLflow.
Full-time
Kjell Garatun-Tjeldstø
CEO
Jarbtech Solution Group
Gil Dror
CTO
Human Care Systems
Larissa Paschyn
Founder
Citizens to the Rescue
Dean Dzurilla
Product Manager
Visible Impact
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