Hire the Best Machine Learning Engineers
Hanoi, Vietnam
Hello, I'm Tam Nguyen Van, a Computer Science graduate from the prestigious Hanoi University of Science and Technology.🎓 Currently, I thrive as a versatile Machine Learning & Deep Learning freelancer, merging a solid foundation in Mathematics and Machine Learning theory with practical programming expertise. My proficiency spans frameworks such as Tensorflow and Pytorch, enabling me to adapt to diverse project requirements seamlessly. Moreover, I boast hands-on experience in deploying Machine Learning models into production environments using REST API, Docker, Flutter, and AWS. 🚀 As a passionate researcher and problem solver in the realm of Machine Learning, I am driven by the challenge of tackling intricate tasks head-on. When you engage me as a freelancer, expect nothing short of timely delivery and impeccable results. My skill set encompasses: • Python. • Tensorflow/Keras, Pytorch, sklearn, pandas. • Machine Learning/Deep Learning. • REST API, Docker. • Flutter. • AWS services. Let's collaborate and bring your projects to life! 🤝
- Machine Learning
- Machine Learning Model
- Deep Neural Network
- TensorFlow
- Computer Vision
- PyTorch
- Natural Language Processing
- Deep Learning
- Keras
- Python
- Data Entry
- Docker
- Amazon S3
- OCR Algorithm
- AWS Lambda
Fogelsville, Pennsylvania
* Expert Vetted talent in Upwork with 100% job success rate. * I am looking for long term work in solving problems with Machine Learning solution. * I have been working on Machine Learning for over 5 years. * My area of expertise in Machine Learning area are: Computer Vision and NLP. * My live projects include: 'Detect Products of Super-market shelves', 'Detect sharp objects from x-ray image', various Image classification models like to classify inside/outside House, Shoes(of different materiel), 'Text classification' of various articles. * I have also worked on stock forecasting LSTM model using stock data and sentiment data. * I have a certification from Udacity in "Self Driving Car Engineer" Nano Degree
- Machine Learning
- Machine Learning Model
- Python
- pandas
- Computer Vision
- Deep Learning
- Keras
- TensorFlow
- Classification
- Model Tuning
- Amazon Web Services
- Deep Learning Modeling
Bahawalpur, Pakistan
🌟 Top Rated Plus AI Engineer | PhD in Computer Science (AI, ML & Generative AI) I specialize in transforming complex ideas into scalable, real-world AI solutions that deliver measurable impact. With a strong blend of research excellence and practical implementation, I help businesses and researchers build intelligent systems that actually work in production. 🧠 Core Expertise 🔹 Computer Vision (YOLO, Vision Transformers, Object Detection) 🔹 Deep Learning (CNNs, Transformers, Vision Transformers - ViTs) 🔹 Generative AI & Large Language Models (LLMs) 🔹 NLP & Fine-tuning (BERT, LLaMA) 🔹 Predictive Modeling & Data Science 🔹Model Optimization & Deployment 💼 Featured Projects 🚧 AI-based Helmet Detection System (YOLOv8 + Vision Transformers) 🌱 Plant Disease Classification using Deep Learning 📉 Customer Churn Prediction System 🧾 Urdu NLP & LLM Fine-tuning Solutions 🏥 Medical Imaging with Explainable AI (Grad-CAM) 🎯 What I Can Do for You ✔ Design and develop custom AI/ML solutions tailored to your business ✔ Build Computer Vision systems (detection, classification, segmentation) ✔ Fine-tune and deploy LLMs & Generative AI applications ✔ Convert research papers into working, production-ready models ✔ Optimize models for performance, scalability, and deployment 🛠️ Tech Stack 💻 PyTorch | TensorFlow | OpenCV | Transformers | Python | Scikit-learn 💡 Why Choose Me? ✨ Top Rated Plus freelancer with a proven track record ✨ Strong PhD-level research + industry implementation expertise ✨ Clear communication, reliability, and on-time delivery ✨ Focus on building accurate, efficient, and production-ready AI systems 📩 Let’s collaborate to bring your AI idea to life! If you’re looking for a dependable expert in AI, Machine Learning, or Generative AI, I’d be happy to discuss your project. Regards 𝑫𝒓. 𝑺𝒂𝒏𝒂 𝑪𝒉𝒆𝒆𝒎𝒂
- Machine Learning
- Python
- Large Language Model
- Image Classification
- GitHub
- Django
- Flask
- Web Application
- Chatbot
- Research Papers
- Academic Editing
- Research Proposals
- LaTeX
- Publication Design
- Professional Journal Citations
Dalhousie, India
I am an AI engineer with 4 years of experience building and deploying production ready AI systems across computer vision, NLP, and generative AI. I have worked on everything clients need right now: fine tuning and training deep learning models, building RAG and GraphRAG pipelines, LLM powered applications, OCR and document extraction, real time face recognition and multi object tracking, classification systems, embedding pipelines, and end to end data workflows from raw input to deployed output. I also have experience with model optimization including distillation, pruning, and quantization for edge and cloud deployment. On the engineering side I am comfortable with FastAPI, PostgreSQL, pgvector, Python async, and cloud and GPU based deployments. I have built and shipped full stack AI products, not just models. I also lead a team of AI engineers, so I understand both deep technical execution and what it takes to deliver consistently on real projects. If you have an AI problem that needs to actually work in production, I can build it.
- Machine Learning
- Artificial Intelligence
- Computer Vision
- Natural Language Processing
- Generative AI
- Large Language Model
- Model Optimization
- Hugging Face
- OpenAI API
- Deep Learning
- Multimodal Large Language Model
- Web Scraping
- LangChain
- LLM Prompt
- LLM Prompt Engineering
- Graph Neural Network
- Research Papers
Tbilisi, Georgia
Hello, I am investing all my time and resources in Upwork ☝ ✅ AWS Certified Solutions Architect Professional ✅ AWS Certified Solutions Architect Associate ✅ AWS Certified Cloud Practitioner I can train, fine tune and deploy production-ready Machine Learning models. *** Degrees *** ✅ Master degree in Automation and Control systems ✅ Bachelor's degree in Engineering Physics. *** Skills *** ➩ AI\Machine Learning--(Scikit-Learn, TensorFlow,HuggingFace,Pytorch) ➩ Programming Languages : Python, C#, Matlab ➩ Microcontrollers--(Raspberry Pi, Arduino) ➩ Mobile Development --(Xamarin) I have experience in hardware as well as the software side of project development. My experience in details is following : AI\Machine Learning 5 years of experience in AI\Machine Learning include: ⬣ PROGRAMMING LANGUAGES : Python, MATLAB ⬣ ML/DL LIBRARIES : TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, OpenCV,Pytorch, HuggingFace ⬣ COMPUTER VISION : 2D,3D Object detection/tracking/pose estimation ⬣ Creating regression and similarity search models *** Certificates*** ✅ AWS Certified Solutions Architect Professional ✅ AWS Certified Solutions Architect Associate ✅ AWS Certified Cloud Practitioner ✅ IELTS certificate Overall Band Score 7.0 Reading 8.5 Writing 6.5 Listening 6.0 Speaking 6.5 ------------------------------------------------------------------------------------------- I AM READY TO IMPLEMENT YOUR PROJECT AND CONVERT YOUR IDEAS INTO A REALITY!
- Machine Learning
- Python
- Deep Learning
- Amazon SageMaker
- PyTorch
- Amazon Web Services
- Cloud Computing
- Google Cloud Platform
- Retrieval Augmented Generation
- AI Agent Development
- Vertex AI
- LangChain
- Large Language Model
- Databricks Platform
- PySpark
Delhi, India
I build AI systems that work in production not demos. 4+ years shipping AI agents, ML models, workflow automations, and full-stack platforms for enterprise clients across the USA, Europe, and India. 30+ projects delivered. 3000+ development hours. 4.9/5 client rating. I'm the founder of an AI development agency and a Software Engineer building government-scale applications so I understand both startup speed and enterprise reliability. 🔹 𝗪𝗵𝗮𝘁 𝗜 𝗕𝘂𝗶𝗹𝗱 ⚡ AI Agents & Multi-Agent Systems — autonomous workflows using OpenAI, LangChain, LangFlow, RAG pipelines, and custom agentic frameworks ⚡ Machine Learning & Data Science — prediction models, NLP, computer vision, model tuning, XGBoost, Scikit-learn ⚡ Workflow Automation — end-to-end business process automation, AI-powered pipelines, GitHub-Slack integrations, report generation ⚡ Full-Stack Development — Python (FastAPI, Flask), React, REST APIs, PostgreSQL, MongoDB, Neo4j ⚡ Cloud & DevOps — AWS (EC2, S3, Lambda, CloudWatch), Google Cloud (GCP), CI/CD pipelines, GitHub Actions, Docker 🔹 𝗦𝗲𝗹𝗲𝗰𝘁𝗲𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 ✅ AI Agent for GitHub-Slack Integration — Built an autonomous AI assistant that lets teams describe features in natural language and auto-generates PRs with multi-step implementation. OpenAI + GitHub MCP + Slack API. ✅ AI-Powered Database Intelligence Platform — Designed and built a SaaS product supporting PostgreSQL, MongoDB, and SQL Server with natural language querying, AI dashboard generation, and security/compliance scanning (GDPR, HIPAA, SOC2). ✅ Project MAGIC (Andromeda) — Led a developer team building web and mobile apps with AI-powered components. Designed CI/CD pipelines, integrated LangFlow and LangChain for intelligent workflows, deployed on AWS. Collaborated with engineers from Meta and GateB. ✅ T2D2 Visual Inspection Platform — Built AI tools for automated structural diagnostics — geolocation extraction from annotated images, IoU evaluation, Human-in-the-Loop system, and automated report generation. ✅ Funding Prediction Model & AI Call Agent — Collaborated with the ex-lead data scientist at Bloomberg. Built ML prediction models, RAG-based AI call agents, and reproducible data pipelines with DVC. ✅ Government-Scale Applications — Built applications for the Ministry of External Affairs handling passport lifecycle tracking, grievance analytics dashboards (React/Redux/Recharts), and reporting systems using Java/Spring Boot with DB2 and Elasticsearch. 🔹 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 Languages: Python, SQL, JavaScript, Java, Bash AI/ML: OpenAI, LangChain, LangFlow, RAG, Scikit-learn, XGBoost, NLP, Computer Vision ,Agno Backend: FastAPI, Flask, Spring Boot, REST APIs, OAuth Frontend: React, Redux, HTML/CSS Databases: PostgreSQL, MongoDB, MySQL, Neo4j, Elasticsearch, Redis Cloud: AWS (EC2, S3, Lambda, CloudWatch), Google Cloud (GCP), Docker, CI/CD, GitHub Actions Tools: Git, Jira, DVC, Agile/Scrum 🔹 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲 → I take full ownership — from architecture to deployment → Production-first mindset — every solution is built for scale, not just a proof of concept → Clear communication — daily updates, documentation, and no surprises → Enterprise experience — I've built systems handling government-scale data and enterprise compliance → Team capability — I lead a team of developers, so I can scale delivery when your project demands it 🔹 𝗜 𝗖𝗮𝗻 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗪𝗶𝘁𝗵 → Building AI agents and multi-agent systems → ML model development, training, and deployment → Integrating OpenAI/GPT APIs into your product → REST API design, development, and integration → Automating business workflows with AI → Full-stack web application development → Cloud infrastructure setup and deployment (AWS & Google Cloud) → Database design and optimization (SQL & NoSQL) → Data science — analytics, dashboards, and insight pipelines Ready to bring your AI or development project to life? Send me a message with your requirements I respond within 2 hours during business hours. Let's build something that works.
- Machine Learning
- Machine Learning Model
- SQL
- Python
- pandas
- Natural Language Processing
- Flask
- Data Cleaning
- MySQL
- Data Analysis
- API
- MongoDB
- Microsoft Power BI Data Visualization
- Data Mining
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Resources to help you hire

Cost to hire a Machine Learning Engineer
Explore typical Machine Learning Engineer rates and what businesses pay to hire top talent.

Machine Learning Engineer job description template
Get tips to write a job post that attracts qualified Machine Learning Engineers.

Machine Learning Engineer interview questions
Top interview questions to help you hire the right Machine Learning Engineers, faster.
Resources to help you hire

Cost to hire a Machine Learning Engineer
Explore typical Machine Learning Engineer rates and what businesses pay to hire top talent.

Machine Learning Engineer job description template
Get tips to write a job post that attracts qualified Machine Learning Engineers.

Machine Learning Engineer interview questions
Top interview questions to help you hire the right Machine Learning Engineers, faster.
Machine learning engineer hiring guide
Machine learning engineers help businesses harness the power of data by designing predictive models, building intelligent applications, and automating complex workflows. Companies hire these professionals to develop recommendation engines, fraud detection systems, demand forecasting tools, and other AI-driven solutions looking to turn raw data into measurable business outcomes.
What does a machine learning engineer do?
Machine learning engineers (MLEs) build and deploy algorithms that help businesses predict outcomes, streamline processes, and unlock value from data. They combine software engineering with data science to develop AI-powered systems that run in production environments.
Freelance MLEs can support everything from deep learning and natural language processing (NLP) to computer vision and model optimization. Machine learning helps companies launch recommendation engines, automate decision-making systems, improve personalization, and more.
Their unique blend of software engineering and data science expertise bridges the gap between experimental models and production-ready systems, making them essential for organizations
How to hire a machine learning engineer on Upwork
Upwork makes it easy to connect with skilled ML engineers for projects of any size. Follow these four steps to hire effectively.
Step 1: Craft a targeted job post
The specificity of your job post directly impacts applicant quality. A well-written job post attracts qualified candidates faster by outlining your goals and tech stack in a clear job description.
Specify the use case. Are you building a forecasting tool, chatbot, fraud detection system, or computer vision app?
List key technologies. Mention frameworks like TensorFlow, PyTorch, scikit-learn, or XGBoost.
Clarify deliverables. Define whether you need a model, full pipeline, or production deployment, and include timelines and budget.
Articulate the scope. Identify expected timeline and budget.
For a faster starting point, try Upwork's Job Post Generator, powered by Uma™, Upwork's Mindful AI. Describe your project in a few sentences and Uma will craft a machine learning engineer job post for your review.
Step 2: Filter and evaluate candidates
Prioritize evidence of hands-on ML work over credentials alone. Use Upwork's filters and search tools to sort by skills, certifications, or industries served.
Review portfolios. Look for completed ML projects, GitHub links, or technical blog posts.
Check ratings and feedback. Past client reviews, high Job Success Scores, and talent badges highlight reliability and communication style.
Shortlist strong matches. Many experts come from bootcamp programs or hold professional certifications.
You can use Upwork’s instant video interviews to screen applicants for a best-fit shortlist, with Uma providing side-by-side candidate comparisons.
Step 3: Interview your top choices
The interview stage reveals how candidates think through complex problems. Prepare relevant machine learning questions to assess fit.
Discuss past projects. Ask how they've handled model performance, bias mitigation, or data quality issues.
Test for real-world thinking. See how they'd approach your dataset or describe tradeoffs between model types.
Check documentation habits. A well-documented model is easier to maintain and scale.
Assess communication skills. Gauge their ability to collaborate remotely.
For deep learning projects, prepare specialized interview questions to assess neural network expertise.
Upwork Messages allows you to schedule and conduct live video interviews on the platform, with call transcripts and summaries available after the calls.
Step 4: Agree on scope and begin work
Once you’ve found the right fit, you can send a contract directly on the Upwork platform. Establishing a shared understanding of milestones and success criteria protects both parties.
Set up payment structure. Use hourly payment for ongoing needs or a fixed-price contract with milestones for projects with defined deliverables.
Break down phases. Use milestones like data prep → model training → testing → deployment.
Agree on evaluation metrics. Whether it's accuracy, AUC, or latency, decide how you'll measure success.
Define revision cycles. Outline how many iterations are included, and use Upwork's tools like contracts and milestones to keep things on track.
Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.
The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.
How much does hiring a machine learning engineer cost?
The cost to hire a machine learning engineer on Upwork ranges from $50-$200 per hour.
Rates vary based on project complexity, model type, and the engineer’s experience level.
When planning your project budget, consider these typical project-based costs for common machine learning engagements:
ML model prototype
$1,000-$3,000 /project
- Build a regression or classification model
- Deliver trained model with documentation
- Provide baseline performance metrics
Custom prediction pipeline
$3,000-$8,000 /project
- Design end-to-end ML pipeline with data ingestion
- Integrate data preprocessing and feature engineering
- Deploy model to staging environment
NLP or computer vision system
$8,000-$20,000 /project
- Develop a custom NLP chatbot or image recognition model
- Implement transfer learning and fine-tuning
- Deliver production-ready API endpoint
Real-time recommendation engine
$15,000-$35,000 /project
- Build a scalable recommendation system
- Integrate with existing product infrastructure
- Conduct A/B testing and performance optimization
Ongoing ML model optimization
$3,000-$8,000 /month
- Retrain and tune existing models
- Monitor model drift and performance
- Implement incremental improvements
FAQs about machine learning engineers
Frequently asked questions
Is hiring a machine learning engineer worth it?
Yes, hiring a machine learning engineer is worth it if you have a clear, data-driven problem to solve and the infrastructure to support it. These specialists help automate decisions, improve predictions, and power personalization features that can lead to measurable improvements in efficiency and revenue.
However, ML engineers are a high-cost investment. If your needs can be met with simpler tools or basic automation, hiring a machine learning engineer may not be cost-effective. Overall, the role delivers the highest value when used on scalable problems where machine learning can directly improve outcomes.
What qualifications should I look for in a freelance machine learning engineer?
Strong candidates for machine learning engineer roles may hold degrees in computer science, statistics, or software engineering; many also complete professional certificates. Prior experience with model deployment, APIs, and production environments often matters more than formal education. Look for someone who understands optimization, system design, and how to apply ML techniques to your industry.
Which tools and techniques should machine learning engineers know?
Most machine learning engineers use Python and tools like TensorFlow, scikit-learn, or XGBoost for tasks ranging from training neural networks to unsupervised clustering. This is how they use specific tools:
Programming languages (Python, sometimes R/Java). Used to build models and data pipelines
ML frameworks (TensorFlow, PyTorch, scikit-learn). For training and evaluating models
Data tools (Pandas, NumPy, SQL). For cleaning, transforming, and analyzing data
Cloud platforms (AWS, GCP, Azure). To store data, train models, and deploy services
MLOps tools (Docker, Kubernetes, MLflow). For versioning, deployment, and monitoring
Data preprocessing and feature engineering. Improve model accuracy by preparing high-quality inputs
Model selection and evaluation. Choose the right algorithm and measure performance
Hyperparameter tuning. Optimize model performance
Model deployment and monitoring. Ensure models run reliably in production and stay accurate over time
Experimentation and A/B testing. Validate model impact in real-world scenarios
Do machine learning engineers also handle data engineering?
Some machine learning engineers handle data engineering, but not all do. While ML engineers often work closely with data engineers, their primary focus is model development and optimization. If your project includes data ingestion or pipeline design, consider hiring a data engineer alongside your machine learning engineer.
What's the difference between a data scientist and a machine learning engineer?
Data scientists focus more on data exploration, statistical analysis, and model prototyping, while machine learning engineers build and scale models that run in production environments. If you need production-ready systems, hire a machine learning engineer.
What's the difference between a machine learning engineer and an AI engineer?
Machine learning engineering and AI engineering are both vital roles, with key differences. A machine learning engineer focuses specifically on building and deploying ML models. An AI engineer takes a broader approach by designing artificial intelligence systems that may include machine learning and also span robotics, rule-based systems, or computer vision.
What kinds of machine learning models can freelance engineers build?
Freelance ML engineers can build models for both supervised and unsupervised learning tasks, including regression models for forecasting, classification models for fraud detection, and clustering models for customer segmentation. They may also build decision trees, KNN, XGBoost, or logistic regression models depending on your objectives.
How can I make sure the machine learning model performs well?
Clear machine learning model performance starts with solid data preprocessing and feature engineering. A skilled engineer will normalize data, handle missing values, and tune hyperparameters to avoid overfitting. During model training, they'll monitor performance with metrics like precision, recall, or ROC-AUC.
Find more freelancers
Similar Machine Learning Engineer Skills
- Generative Model Specialists
- Transfer Learning Specialists
- Machine Learning Model Specialists
- Deep Learning Experts
- Keras Professionals
- fastText Specialists
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- TensorFlow Specialists
- Image/Object Recognition Professionals
- Reinforcement Learning Specialists
- Data Augmentation Specialists
- Supervised Learning Specialists
- Object Detection Specialists
- PyTorch Specialists
- Deep Neural Networks Developers
- Transformer Model Specialists
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