Hire ML Developers

Build scalable ML products with Uptech’s expert engineers, delivering reliable models and production-ready solutions to meet your business needs.

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Our ML Developers to Hire

5 years of experience

•

Ukraine

Oleh Komenchuk

ML Department Lead

Domain expertise

Healthcare, E-commerce, Enterprise, Entertainment

Tech stack expertise

Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Generative Models, Computer Vision, OCR, MLOps

Hire Oleh

3 years of experience

•

Ukraine

Artem Havryliuk

ML Engineer

Domain expertise

Enterprise, E-commerce, Scientific Research

Tech stack expertise

Machine Learning, Deep Learning, NLP, Generative Models, Computer Vision, MLOps, Data Engineering

Hire artem

Our AI/ML Tech Stack

ML Algorithms and Approaches

AI Agents
RAG
LLMs
Generative AI (Stable Diffusion, LoRA, kohya-ss, ComfyUI)
NLP (Text Classification, Sentiment Analysis, Text Summarization, Text Generation, etc.)
Computer Vision (Image Classification, Image Segmentation, Object Detection, OCR, etc.)
Autoencoders
GANs
Diffusion Models
Transformers
Neural Networks
Deep Learning
Probabilistic ML
Reinforcement Learning
Unsupervised Learning (Clustering, Outlier Detection, etc.)
Semi-Supervised Learning
Supervised Learning (Classification, Regression, etc.)

Languages

Python
SQL
C++

ML Frameworks

Scikit-learn
SciPy
Tensorflow & Keras
PyTorch
PyTorch Lightning
XGBoost/LightGBM
LangChain
LlamaIndex
Transformers
SentenceTransformers
PEFT
LoRA
Kohya-ss
ComfyUI
OpenAI API
Azure OpenAI API
Anthropic Claude/Mistral/Google Gemini APIs

ML Tools

PyCharm
DataSpell
Jupyter Lab/Notebook
Google Colab
AWS Sagemaker

ML Environment & Packaging Tools

Docker
Conda
Poetry/pip
DVC (Data Version Control)
Git

MLOps & Deployment

FastAPI
Streamlit/Gradio
Docker
AWS
Azure
Weight & Biases
Neptune.ai
MLflow

Hire ML Developers for Custom Development Services

Back-end
Development

With over 8 years of experience, we offer expert ML consulting to address real-world business challenges. From product discovery and goal setting to feasibility studies and technical assessments, we provide a strategic foundation for solutions that meet your budget, timeline, and long-term objectives.

Data Engineering
Services

We help you collect, refine, and manage data to fuel AI and ML solutions. Our team builds scalable, cost-efficient infrastructure for everything from storage to real-time processing, ensuring reliable data pipelines that support your business growth while keeping costs low.

ML Proof-of-Concept
Development

We transform your ideas into actionable proof of concepts (PoCs), defining goals, setting KPIs, and assessing feasibility. Our ML experts handle data design and development, identifying risks early and aligning each PoC with your long-term roadmap, whether for an app, tool, or cloud-based solution.

ML Model Development and Integration

Our full-cycle ML development focuses on creating scalable, production-ready models tailored to your needs. We ensure seamless integration, long-term maintainability, and continuous optimization of data and models to meet your evolving requirements.

ML Model Testing and
Fine-Tuning

We rigorously test and fine-tune ML models using real-world data to ensure peak performance. Our team focuses on edge cases, minimizing biases, and optimizing model robustness, providing actionable insights for continuous improvement and accuracy.

Skilled AI/ML Engineers
On-Demand Hiring

Access skilled AI/ML engineers through our flexible, on-demand hiring process. With customizable plans and experts experienced across various domains, we help you scale your team quickly and confidently without compromising quality.

Our ML Developers Hiring Process

Initial Consultation

We begin by thoroughly discussing your ML solution needs, including key factors like the tech stack, project scope, timeline, and KPIs. This helps us fully understand your business goals and select the ideal developers for the job.

Interview with ML Developer

Once the project requirements are defined, we present a curated list of freelance machine learning developers. We encourage your active participation in the selection process to ensure the best fit for your team.

Cooperation Model Selection

At Uptech, we offer flexibility in team structure. You can either augment your existing team or opt for a dedicated team to manage the entire ML product development. We ensure constant updates and alignment with your budget, timeline, and KPIs.

Onboarding and Support

We ensure smooth team integration and proper talent onboarding based on your chosen cooperation model. As the project progresses, we keep you updated on milestones toward your business goals. If your needs change, our team will adapt accordingly, with your satisfaction as our top priority.

Our Successful Cases

Presidio investors

AI-Powered Investment Data Automation

A system that leverages AI to analyze, structure, and automatically upload deal and investment data into your CRM—reducing manual work by 80% and handling up to 100 deals per day.

View Case Study

Angler AI

AI-Powered Prospect Data Platform

An AI-driven SaaS web app that helps brands boost customer acquisition and lifetime value by creating targeted audiences, distributing them to ad platforms, and providing precise ROI analytics—all through an intuitive workflow.

View Case Study

AboardAI

Your Personal AI Flight Assistant

An iOS app that captures real-time aircraft data and uses ML to detect flight phases, flap positions, and transcribe in-flight speech—delivering actionable insights to pilots in a clear, non-distracting interface to enhance safety and efficiency.

“Uptech is a great partner for software and web development projects. I was impressed with the talent level for each of the roles, including design, front-end, back-end.”

Indy Sheorey CO-FOUNDER & CTO, ANGLER AI

Looking for expert ML developers?

Share your requirements with us and we’ll find the best match to meet your business needs

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In recent years, we’ve witnessed the explosion of data, the harder push for automation, and the rising demand for personalized user experiences. In response, businesses are racing to adopt machine learning and hire AI developers to unlock new growth opportunities.ML has not only shaken businesses in tech, but also in finance, healthcare, e-commerce, and beyond.

The Machine learning market is following an upward trend and is projected to have a compound annual growth rate of 38.8% by 2029.

Given this, businesses recognize how crucial it is to hire dedicated ML developers now more than ever. Bringing the right ML Engineers on board is a strategic investment to drive innovation and leverage the technology’s long-term value.

Want to do it right? Read on to discover how to hire an ML developer to meet your business needs.

What Does a Machine
Learning Developer Do?

Simply put, machine learning developers design algorithms that recognize patterns and create models that learn and evolve from the data fed to them. They develop models that “get smarter” over time. Here’s a high-level overview of what an ML developer generally does:

Design and implement learning algorithms
Train models for prediction and pattern recognition

Develop real-world applications such as recommendation systems, fraud detection, and predictive maintenance
Test and optimize model performance.

Key Responsibilities of Machine Learning Developers

A machine learning developer’s job does not end with creating algorithms and models. Here are some of their key day-to-day responsibilities:

Gathering, cleaning, and preprocessing large datasets.
Designing and developing machine learning models.
Experimenting with algorithms and hyperparameter tuning.
Experimenting with algorithms and hyperparameter tuning.

Monitoring model performance and retraining when needed. Developers keep an eye on the model’s performance after deployment. Based on the model’s outputs, ML developers handle adjustments and retraining as necessary.
Collaborating with data engineers, analysts, and product teams. ML engineers work side by side with other teams to align models with business objectives.

Common Specializations in Machine Learning

Machine learning is a diverse field, and each specialization requires deep expertise to implement successfully. This can make it difficult to hire machine learning engineers since you need a solid understanding of which specialization is essential for your project.

Here are some of the common specializations in ML:

Supervised & Unsupervised Learning

Supervised and unsupervised learning differ based on whether they use labeled or unlabeled data. Labeling data involves identifying each piece of information according to established rules. In supervised learning, the developer trains a model with labeled data for classification (e.g., spam detection, image recognition). Meanwhile, unsupervised learning uses unlabeled data for input and output (e.g., models that utilize user data/preference to recommend products or content).

Deep Learning

Deep learning involves using artificial neural networks to learn from data. This type of machine learning aims to solve more complex problems that span from object recognition to natural language processing (NLP).

Reinforcement Learning

Reinforcement learning (RL) is a machine learning technique where the software learns optimal results through trial and error. Some examples of this include powering AI in games, robotics, and self-driving vehicles.v

Time Series Forecasting

This ML approach focuses on predicting future values and trends over time using historical data. Some examples include stock and weather forecasting.

Recommendation Systems

If you've ever received Amazon product recommendations after expressing interest in them on another app, you've witnessed a recommendation system in action. These systems utilize users’ preferences or behaviors to suggest items or content.

ML Developers vs. AI Developers – Key Differences

Although often used interchangeably, Machine Learning (ML) developers and AI developers have distinct focuses:

ML Developers

ML developers specialize in data-driven statistical modeling and creating algorithms that learn from data and improve over time.

AI Developers

AI developers build systems that can "reason," not just predict based on data. This role encompasses a broader range of artificial intelligence technologies, including symbolic AI, computer vision, natural language processing (NLP), and generative models.

When Your Business Needs to Hire ML Engineers?

Not sure how machine learning can benefit your business? Here are the clear signs it’s time to hire dedicated machine learning developers:

You’re sitting on large volumes of unused customer data

Do you have a goldmine of customer data with untapped potential? ML development can expedite the extraction of valuable insights and analytics from huge amounts of data.

You want to personalize content or product recommendations

Hire AI developers if your business model significantly benefits from creating a more tailored user experience or product recommendations.

You need predictive
insights

Whether it’s forecasting sales, predicting churn, or identifying trends, machine learning provides data-driven insights to support smarter business decisions.

Your operations could benefit from automation

Does your team spend hours on analytics and reporting? Machine learning can streamline processes, minimize manual tasks, and save both time and resources in the long run.

What Skills Should You Look for in a Machine Learning Developer?

To hire dedicated machine learning developers, you need to look for a balance of both technical expertise and soft skills. Here are the main qualifications to consider:

Technical Skills

Proficiency in Python, R, or Julia - the leading languages used for ML development.
Experience with ML libraries like scikit-learn, XGBoost, and LightGBM
Familiarity with deep learning frameworks such as TensorFlow, PyTorch, and Keras
Strong foundation in statistics, linear algebra, and probability
Expertise in model evaluation, validation techniques, and A/B testing
Knowledge of MLOps tools (e.g., MLflow, DVC, Kubeflow)
Experience with cloud platforms like AWS SageMaker, Google Vertex AI, or Azure ML

Soft Skills

Strong critical thinking and problem-solving abilities
Clear communication skills to explain technical results to non-technical stakeholders
Curiosity and willingness to learn continuously - Given how fast the ML field evolves, top developers don’t shy away from upskilling themselves to stay competitive.
Keep in mind that these skills may not cover everything your ideal hire needs. Consider additional qualifications based on your project requirements, or consult with hiring experts or ML agencies like Uptech for a more tailored fit.

How to Hire the Best Machine Learning Developers?

Identify Your Needs

Be clear on the scope of the role. Define the tech stack, the level of expertise, and any other important factors based on your project requirements

Source Talent

You can use job listing platforms or professional networks like LinkedIn to start searching for talents. You can also consider partnering with ML agencies like Uptech to access a pool of vetted ML developers.

Conduct Interviews and Assess Your Candidates

Review portfolios and assess candidates’ skills based on your specific qualifications for the role.

Agree on a Reasonable Compensation

Make a fair offer that reflects the candidate’s qualifications, and be open to negotiations that benefit both parties.

How to Assess Machine Learning Developers During the Hiring Process?

Before you hire AI developers, assess your candidates using this skeletal guide to gauge their technical and functional ML capabilities:

Portfolio Review

Look for end-to-end ML projects that show real-world impact. A strong portfolio shows their ability to take projects from concept to deployment.

Technical Interview

Test their understanding of key concepts like algorithms, overfitting, and model tuning. Ask them to explain how they apply these approaches in their development process.

Coding Test

Give them a task involving model building, data preparation, or other core ML tasks to assess their coding skills on the spot.

Problem-solving Exercise

Present a real business problem and ask them to propose a suitable ML solution. This exercise evaluates their creativity and analytical skills in a business context.

What Questions Should You Ask a Machine Learning Developer in an Interview?

Interviews typically last about an hour, so it's important to ask both technical and experience-based questions to get the most out of the limited time. We’ve listed some of the baseline questions to assess your candidate’s credentials effectively:

Technical Questions

How do you choose the right ML algorithm for a problem?
Explain overfitting and how you would address it in model development.
What’s your approach to feature selection and engineering?
How do you evaluate model performance and adjust it accordingly?

Experience-Based Questions

Tell us about a model you built and deployed into production. What challenges did you face?
How do you manage data quality issues, especially with incomplete or noisy data?
Describe a time when a model underperformed. How did you identify and resolve the issue?

How Much Does it Cost to Hire a Machine Learning Developer?

The cost to hire ML developers varies depending on experience, project scope, and hiring type:

How Can You Ensure a Successful Machine Learning Development Partnership?

While ML agencies guide their clients from planning to development, a successful partnership requires you to set clear expectations before going into the initial consultation. Ensure a smooth-sailing collaboration by doing the following:

Set clear business goals and success metrics. Define measurable goals with the ML agency from the get-go (e.g., X percentage increase in productivity due to automated tasks).
Provide access to quality data. ML model outputs rely on the quality of its data. Before starting the project, give the ML team access to clean, high-quality datasets if possible.
Ensure regular communication between ML talent and stakeholders. Regular check-ins (e.g., Daily Scrums) between the ML talent team and stakeholders allow the teams to raise issues they encounter and resolve them right away. Constant communication keeps the tasks synced up with the business goals.
Be ready for iterative development and testing. ML development may involve repetitive development and testing for cleaner outputs and model improvements.
Promote collaboration between domain experts and tech teams. The ML team also needs to collaborate with domain experts in every project milestone. This way, they can verify that the project scope and features align with their expectations.

For a successful partnership with an ML agency, your business needs to set clear goals and quantifiable metrics. The ML team also needs regular sync-ups with the other teams involved in the different phases of development, such as the domain experts and stakeholders. Lastly, the ML developers need access to high-quality data to produce the best output possible.

Ready to
collaborate?

Let’s advance your next project together