Need to Outsource Machine Learning Services? Here Are Some Top Providers Who Can Help

Machine learning (ML) was once the domain of tech giants and research labs, but with AI becoming more accessible, demand for ML expertise has skyrocketed. Businesses across industries now rely on ML for automation, data insights, and enhanced customer experiences.

But here’s the challenge: machine learning talent is in high demand, and building an in-house team takes time. That’s why many companies outsource machine learning services to access the expertise they need.

In this guide, we’ll break down why companies outsource ML services, which ML tasks can be outsourced, and the best machine learning service providers who can help. 

Why Do Companies Outsource Machine Learning Services?

The demand for machine learning expertise is at an all-time high, but finding the right talent remains a challenge. A recent McKinsey survey highlights that AI adoption is reshaping workforce needs, with machine learning engineers among the most difficult roles to fill. 

These are the most common reasons why businesses choose to outsource ML services.

• Cost reduction: Gain access to top-tier ML talent without the overhead of full-time employment.
• Faster project execution: Pre-vetted ML teams can start immediately, cutting down development time.
• Flexibility & scalability: Scale teams up or down as needed, avoiding long-term commitments.
• Access to niche expertise: Work with specialists in computer vision, NLP, and predictive analytics.
• Reduced hiring burden: Avoid the challenges of recruiting highly skilled ML engineers.

For many businesses, building an in-house ML team isn’t always practical (or necessary). High salaries, long hiring timelines, and fluctuating project demands make outsourcing to machine learning engineers a cost-effective and flexible alternative. 

What Machine Learning Services Can Be Outsourced?

There isn’t a one-size-fits-all approach to outsourcing machine learning. You can choose to outsource specific ML functions for individual projects, fill particular skill gaps on your team, or hand over entire initiatives to external experts.

This flexibility means you can maintain control where it matters most while leveraging outside talent for specialized tasks.

Here are some of the most common ML services that companies typically outsource, either as standalone needs or as part of larger AI initiatives:

• Data labeling and preprocessing: ML models rely on high-quality data, but cleaning, annotating, and structuring datasets is time-consuming. Outsourcing means accurate, well-labeled data without pulling internal teams away from core work.
• Predictive analytics: Businesses use ML models to forecast trends, customer behavior, and risks. Outsourcing gives companies access to experts who fine-tune models for faster insights and better decision-making—without needing a dedicated team.
• Computer vision: From self-checkout systems to medical imaging AI, computer vision powers automation across industries. Developing these models requires deep expertise and extensive training data, making outsourcing a cost-effective solution.
• Natural Language Processing (NLP): Behind chatbots, voice assistants, and sentiment analysis, NLP solutions require specialized linguistic and data science expertise. Outsourcing NLP development means businesses get sophisticated AI without maintaining an in-house team.
• Custom AI model development: Some projects demand fully custom ML models, requiring data science expertise and extensive computational resources. Outsourcing allows businesses to build tailored AI solutions without investing in expensive infrastructure.

If you’re considering outsourcing machine learning or AI development, working with a full-service outsourcing company like the ones listed in the next section can streamline the process.

But that’s not your only option.

You could also bring in specialist expertise by hiring ML engineers on a contract basis or hiring ML engineers offshore—giving you the same cost savings as outsourcing while maintaining direct control over your ML initiatives.

The companies in the next section represent both approaches, from full-service outsourcing providers to recruitment specialists who can help you build your own ML team.

6 Companies to Consider for Your Next Machine Learning Project

If you’ve decided that outsourcing your machine learning is the right move but aren’t sure where to begin, these companies are a great starting point.

Chosen for their industry reputation and accessibility to US businesses, this list includes both full-service ML providers capable of handling entire projects and staffing agencies that help businesses hire machine learning engineers.

This list isn’t ranked but highlights some of the best machine learning service providers for businesses looking for expert support.

Whether you need specialized ML engineers or want to fully outsource your AI development, partnering with a trusted ML provider can lead to faster execution, cost savings, and access to top-tier expertise. 

1. Near

We’re up first, and that’s because we understand that finding the right machine learning engineers doesn’t necessarily mean hiring locally. At Near, we specialize in sourcing top ML talent from Latin America, giving businesses a cost-effective edge over those relying only on local hires.

We also know that while technical skills matter most, seamless team integration is just as important for long-term success. That’s why we connect companies with ML engineers who align with their tech stack, company culture, and project goals.

With Latin America’s AI sector growing rapidly, we help businesses access highly skilled engineers in under 3 weeks while keeping hiring costs under control. 

Key features:

• Full-service support: We handle everything from sourcing and screening to contracts, payroll administration, and local compliance—removing all the complexity of international hiring.
• Pre-vetted nearshore talent: Every ML engineer candidate undergoes technical and English proficiency assessments and reference and international criminal background checks.
• Transparent, low-risk pricing: Interview candidates for free, then pay a monthly fee per hire once you’ve found your match. Replacement guarantee in case of compatibility issues. Most clients save 30–70% compared to equivalent US salaries while offering competitive compensation for LatAm professionals.

2. Harnham

Harnham is a specialist recruitment firm focused on data science, machine learning, and AI talent acquisition. A UK-based company with offices in New York, San Francisco, and Phoenix, it connects US businesses with contract, full-time, and executive-level ML professionals. 

It specializes in roles like ML engineering, ML Ops, NLP/LLMs, and AI architecture, making it a strong choice for companies looking to expand their in-house teams.

With a deep understanding of the AI and ML hiring landscape, Harnham provides access to highly sought-after specialists in a competitive talent market.

Key features:

• Exclusive ML talent pool: Maintains a curated network of pre-vetted AI and ML professionals, reducing hiring time.
• Flexible hiring models: Offers contract, full-time, and executive hiring options tailored to business needs.
• Industry expertise: Specializes in placing AI and ML professionals across various industries.

3. Redfish Technology

Redfish Technology is a US-based recruitment agency specializing in machine learning and AI talent acquisition. Founded in 1996, it has built a network of ML professionals across tech hubs nationwide. 

It offers a hands-on search process, delivering pre-screened candidates within four weeks. Its AI-driven sourcing approach and deep industry knowledge make it a solid choice for companies looking to hire machine learning engineers, AI architects, and MLOps specialists.

Key features:

• Fast hiring process: Typically delivers 5+ qualified ML candidates within the first week of the search.
• Hands-on vetting: Uses a custom screening process tailored to each company’s hiring needs. Candidates are assessed for both technical skills and industry-specific expertise before being shortlisted.
• Dedicated recruitment support: Assigns a single point of contact and a recruiter working 20+ hours weekly per client.

4. NI-X

N-iX is a global AI and machine learning solutions provider with a strong US presence, including an office in Miami. It offers both full-service ML development and offshore staff augmentation through its teams in Eastern Europe. 

It delivers custom AI models, computer vision, NLP solutions, and MLOps services. N-iX follows a structured AI adoption framework that ensures that AI and ML solutions are designed for long-term scalability. 

Its process includes use-case qualification, architecture design, and model lifecycle management to align AI development with business objectives.

Key features:

• End-to-end ML services: Covers everything from strategy and PoC to full AI/ML deployment and maintenance.
• MLOps and AI maintenance: Provides continuous monitoring, retraining, and model governance for stable performance.
• Offshore ML talent: Offers Eastern European AI specialists as a cost-effective alternative to US hiring.

5. Thinkitive

Thinkitive is an India-based machine learning outsourcing provider with a US presence. It offers full-service ML development and staff augmentation, giving businesses flexible engagement models. 

With 22+ dedicated ML developers and 33+ completed projects, Thinkitive delivers custom AI models, NLP solutions, time series forecasting, and MLOps support. 

Its tech stack includes Azure Machine Learning, AWS SageMaker, Google AI Platform, and leading ML frameworks like TensorFlow and PyTorch for scalable AI solutions. It also utilizes Scala, Python, R, C, and leading proprietary and open-source ML frameworks. 

Key features:

• Comprehensive ML development: Covers data preprocessing, model training, deployment, and long-term monitoring.
• End-to-end AI support: Provides ongoing model optimization, retraining, and performance monitoring post-deployment.
• Timezone flexibility: While based in India, Thinkitive provides timezone flexibility for US-based businesses looking to integrate ML without hiring in-house.

6. iTech Data

iTech Data is a US-based full-service machine learning outsourcing provider specializing in AI-driven document processing and automation. iTech’s AI algorithms improve accuracy, reduce manual workloads, and enhance process transparency. 

Its blueprint and engineering drawing AI solutions allow for easy data extraction, meaning businesses can digitize and automate complex document workflows at scale.

Key features:

• Consultative approach: Provides guidance throughout the project from initial consultation to deployment and support for full-service offerings.
• Industry-specific AI solutions: Supports finance, oil & gas, logistics, and engineering document processing and capture.
• OCR and RPA for engineering projects: Robotic process automation and optical character recognition services for classification and indexing on physical and field builds and projects.

Final Thoughts

With over 60% of businesses using generative AI, many clearly see the value of ML but lack the in-house expertise to develop and deploy custom AI solutions.

The question isn’t whether machine learning can drive value—it’s how to access the right talent efficiently. Whether you’re looking to build predictive models, implement computer vision, or develop NLP capabilities, the key is finding an approach that balances cost, control, and expertise.

For many US businesses, the traditional options of building an entirely in-house team or completely outsourcing development both come with significant tradeoffs. This is where nearshore hiring from Latin America strikes the perfect middle ground—you get dedicated engineers who work as true team members, maintain full control over your development process, and still achieve the cost savings that make machine learning initiatives financially viable.

The machine learning landscape continues to evolve rapidly, with new frameworks, methodologies, and applications emerging constantly. Businesses that find the right approach to accessing ML talent position themselves to leverage these innovations without the recruiting challenges and high costs that typically accompany cutting-edge technology adoption.

Want to learn more about how nearshoring can help you access the top talent you need while controlling costs? Read our guide, “Nearshoring: The Smartest Way to Cut Costs and Scale Your Business in 2025” to explore the benefits of this increasingly popular hiring strategy.