Go beyond basic training with ML learning experts who understand real-world user load. We build production-ready MLOps pipelines that let your AI solutions launch on time and perform reliably.
Hire Machine Learning engineers trusted to run ML where it matters to boost prediction accuracy, accelerating delivery, and powering automation across complex enterprise environments.
From data preprocessing and feature engineering to model deployment and monitoring, our experts take ownership of your entire machine learning lifecycle.
Hire senior machine learning developers that craft complex predictive models using Python, TensorFlow, and PyTorch to support high-dimensional data analysis and pattern recognition.
Hire Machine Learning engineers for smooth connectivity with AWS SageMaker, Azure ML, or Google Vertex AI, allowing scalable training and deployment workflows.
Get the right talent for your specific needs. Whether you need a Data Scientist for research or an ML Ops Engineer for productionization, we provide flexible options.
Drive sustained model performance through automated retraining pipelines, hyperparameter optimization, and drift detection mechanisms.
Hire Machine Learning engineers with hands-on skills in Scikit-learn, Keras, and Big Data frameworks. Entrans provides top-tier professionals to build, configure, and scale your intelligent applications.
Our team builds future-proof logic. Hire Machine Learning engineers to develop regression and time-series models that forecast sales, demand, and market trends with high precision.
Hire Machine Learning engineers to process and analyze text data. We handle sentiment analysis, chatbots, and text classification using libraries like SpaCy, NLTK, and Hugging Face Transformers.
Our experts build visual intelligence. Hire Machine Learning developers to implement object detection, facial recognition, and image segmentation using OpenCV and CNNs (Convolutional Neural Networks).
We execute rigorous validation cycles. Hire Machine Learning engineers to perform cross-validation, A/B testing of models, and bias detection to ensure ethical and accurate AI performance.
Entrans’ ML engineers manage containerized deployments. We use Docker and Kubernetes (Kubeflow) to serve models via APIs, ensuring low-latency inference and high availability.
We establish comprehensive data strategies. Hire Machine Learning engineers skilled in ETL pipelines and feature selection to make sure your algorithms are fed with clean, structured, and relevant data.
Technical Stack: Next.js (SSG), Strapi, Tailwind CSS, Vercel
A leading Fintech services company was limited by its slow, monolithic WordPress site, which hurt its SEO and content workflow. We migrated them to a headless architecture with Next.js for Static Site Generation and Strapi as the CMS.
The new platform had better functionality and a better UX for users uploading bills, statements, and receipts for reconciliation.
Technical Stack: Python, Flutter, Angular, LangChain, FastAPI
Enterprises needed a secure AI solution for human-like conversations grounded in their own proprietary data. We developed Thunai, our enterprise-grade agentic platform, to automate sales and support workflows.
Thunai delivers accurate, data-driven responses, handles complex agent tasks, and provides a unified support experience in over 150 languages.
Entrans provides specialized Machine Learning development teams that ensure algorithm efficiency, data security, and rapid prototyping. Our pre-vetted network offers deep expertise in Neural Networks, Random Forests, and Statistical Modeling, delivered through adaptive engagement models that integrate seamlessly with your existing data infrastructure.
Identify high-impact opportunities and datasets that need ML Engineer roles.
Access pre-screened profiles with verified degrees in Data Science and hands-on project experience.
Validate expertise in Algorithm Selection, Data Wrangling, and Model Deployment through rigorous technical challenges.
Onboard experts who understand your data privacy and compliance needs (GDPR/HIPAA), preventing ramp-up delays.
Maintain continuity with structured upskilling on the latest SOTA (State of the Art) models and techniques.
Ideal for long-term product development and continuous model improvement.
Scale your data science team with specialized ML consultants as an extension of your workforce.
Hire for specific tasks, such as building a POC, data cleaning, or deploying a specific algorithm.
Our team serves global clientele, specializing in Healthcare, Finance, and Retail automation. Our specialists architect intelligent solutions using their deep knowledge of the Python and Data Science ecosystem.
A Machine Learning engineer is a programmer who designs and builds software that can learn from data. They sit at the intersection of Data Science and Software Engineering, taking models from research to production.
Essential skills for ML engineers include proficiency in Python and frameworks like TensorFlow or PyTorch, alongside a strong mathematical foundation in linear algebra, calculus, and statistics.
To automate complex decision-making and gain predictive insights. Companies hire machine learning developers for your models that are not just accurate in the lab, but scalable, efficient, and valuable in a production environment.
Freelance or contract rates for US-based machine learning engineers generally fall between $60 and $200 per hour. That said, offshore talent from Eastern Europe (e.g., Poland, Ukraine) averages $35–$80 per hour, while skilled engineers from India offer the most competitive rates at $20–$50 per hour.
Machine Learning is a subset of AI rather than a competitor. However, AI is the broad science of mimicking human abilities, where ML is the specific method of training algorithms to learn from data.