How to Hire AI Engineers & Developers: The Complete Guide
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HR & recruitment
Top Rated on Upwork
15+ Years of Combined Expertise
100% Job Success on Upwork
Vetted ML Engineers
Production-First ML Systems
Fast Onboarding
No Long Hiring Cycles
Hire Machine Learning Engineers & Developers
Build and scale intelligent solutions with experienced professionals when you hire machine learning engineer talent with expertise in model development, MLOps, NLP, computer vision and production ML systems. Get the right expertise on board and start within 24 hours.
Let's Build Your AI Team
100+
AI Developers
160+
Software Products Delivered
50+
AI Solutions
15+
Total Years of Experience
Our Trusted Clients
Meet Our Engineers
Meet Nagadia
0.7 Years of ExperienceSenior AI Engineer/Developer
- M.Sc. in AI AURO Univeristy - Surat, India
- B.Sc. in IT B.K.Birla - Mumbai, India
NLP
Machine Learning
Artificial Intelligence
Data Analysis
Data Science
Back-end Developers
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL
Analytics DevelopmentNLP
Machine Learning
Artificial Intelligence
Data Analysis
Data Science
Back-end Developers
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL
Analytics Development
Dev Chandan
3 Years of ExperienceSenior AI Engineer/Developer
- Post Graduate Program in Artificial Intelligence & Machine Learning, University of Texas at Austin (in collaboration with Great Lakes), 2025 — GPA: 3.72/4.0
- B.Tech in Artificial Intelligence, NMIMS Mukesh Patel School of Technology Management & Engineering (in collaboration with Virginia Tech), 2024 — CGPA: 3.93/4.0; Certificate of Merit
Generative AI
Large Language Models (GPT, Claude, Gemini, Llama, DeepSeek)
RAG
LangChain
Prompt Engineering
Function Calling
Fine-tuning (QLoRA)
Python
FastAPI
SQL
REST APIs
PyTorch
TensorFlow
Hugging Face
Kubernetes
Docker
Azure
GCP
GitHub Actions
Langfuse
LangSmith
Pinecone
Chroma
YOLOv8
OpenCV.
Sushant Upadhyay
3 Years of ExperienceSenior AI Engineer/Developer
- B.Sc. in Statistics Kishanchand Chellaram College - India
- M.Sc. in Big Data Analytics Jai Hind College - India
NLP
Machine Learning
Reinforcement learning
Artificial Intelligence
Big Data Analysis
Data Science
Back-end Developer
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL, Cassandra, MongoDB, GraphQL
Scala
Pyspark
TaraChand Vishnoi
5 Years of ExperienceAI Consultant
- Lovely Professional University
- Bachelor of Technology - BTech, Mechanical Engineering
NLP
Machine Learning
Artificial Intelligence
Data Analysis
Data Science
Back-end Developers
Neural Network
Data Visualization
LLM
OpenAI
RAG
Prompt Engineering
Python
SQL
Analytics Development
Diego Jimenez
14 Years of ExperienceUS Business Partner
- EAE Business School, Spain MBA - Master in International Business, International Business Minor - Design Thinking
- University of Leeds, United Kingdom Post Grad certification How to Choose the Right Problem to Solve
- Nebius Academy AI Agents and Automation Certification AI for Managers
- Politecnico de Suramerica, Colombia Applied Psychology of Sales
- AIEP Universidad Andres Bello, Chile Bachelor’s degree on Business Administration with an emphasis on Finances
Agentic AI
Development
AI Strategy
AI Transformation
Business Operations
Process Automation
AI Agents and Automation
Business Transformation
Revenue Analysis
Strategic Business Development
B2B Marketing Strategy
Growth Strategies
Sales Strategy
CRM Sales Management
International Business
Aamesh Gori
1.5 Years of ExperienceAI Engineer
- University of Mumbai - Bachelor's in Computer Engineering
AI Security
AI Governance
Agentic AI
AI Automation
Optical Character Recognition (OCR)
Retrieval-Augmented Generation (RAG)
Large Language Models (LLM)
Python
AWS
Azure
Kubernetes
Docker
Our Technical Expertise
Core Capabilities of Our ML Engineers
Hire expert machine learning engineers to build, deploy and scale production-ready ML systems across your business needs.
Custom ML System Development
Designing and building tailored machine learning systems aligned with your data, business goals and performance requirements, with scalability and reliability built in from the start.
Data Pipelines & Feature Engineering
Building reliable data pipelines and feature engineering workflows that prepare high-quality data for consistent model training, validation, and inference.
Model Training, Evaluation & Optimization
Training and refining ML models with rigorous evaluation, advanced algorithms, and optimization techniques to improve accuracy, efficiency, and real-world performance.
MLOps & Production Deployment
Taking ML models from development to production with MLOps practices covering CI/CD, model monitoring, drift detection, and automated retraining.
Generative AI & LLM Integration
Integrating LLMs, RAG pipelines and generative AI capabilities with business data and existing ML infrastructure to support practical production use cases.
ML System Modernization
Modernizing legacy ML and analytics workflows, moving experimental models into production, and introducing automation for more efficient, maintainable ML operations.
What We Build
Machine Learning Solutions We Build
01
Predictive Analytics Systems
Build forecasting and classification models for demand prediction, churn analysis, risk assessment, and data-driven business decisions.
02
NLP & Text Intelligence Applications
Develop solutions for sentiment analysis, entity extraction, document classification, text processing, and conversational AI.
03
Computer Vision Systems
Create image and video processing systems for object detection, facial recognition, medical imaging, and automated quality inspection.
04
Recommendation & Personalization Engines
Build personalized experiences with recommendations and matching systems based on user behavior, content, and product data.
05
LLM-Powered ML Applications
Integrate large language models into production applications for chatbots, content generation, knowledge retrieval, and intelligent automation.
06
MLOps & Model Infrastructure
Build end-to-end ML pipelines with automated training, model versioning, monitoring and scalable model serving.
07
Fraud & Anomaly Detection Systems
Develop real-time ML models that identify unusual patterns, detect potential fraud, and help protect business operations.
08
Custom ML Models for Enterprise
Build bespoke models trained in your proprietary data and tailored to your industry, use case, data environment and performance goals.
How We Work
Choose the Right ML Engagement Model
Dedicated Machine Learning Engineer
A machine learning engineer for hire can integrate directly with your team and take ownership of model development, training and continuous improvement.
ML Team Augmentation
Strengthen your existing data or engineering team with experienced ML talent to close skill gaps, accelerate development and move models into production faster.
Project-Based ML Development
Have a defined ML project in mind? We handle development from scoping and model building to deployment and documentation with a clear scope and timeline.
Flexible / Part-Time Engagement
Bring in senior ML expertise when you need it for model reviews, MLOps, architecture guidance or focused development sprints.
Why Choose us
Why Businesses Hire Machine Learning Engineers from Us
01
Production-Grade ML Experience
Our engineers have hands-on experience building and deploying ML systems in production, covering feature engineering, model training, MLOps and ongoing monitoring. They focus on building reliable systems that perform beyond the development environment.
02
End-to-End ML Development
From data pipelines and model development to deployment, monitoring and retraining, our engineers can support the complete ML lifecycle. This reduces handoffs and keeps development aligned from the first model to production.
03
Hire in 24 Hours
Share your requirements and hire ML engineer talent matched to your project, ready to start within 24 hours without lengthy hiring cycles. Start addressing your ML requirements sooner without waiting through traditional recruitment processes.
04
US Time zone Availability
Our engineers work across EST and PST, making it easier for US businesses to collaborate, communicate, and make decisions in real time. Stay aligned with your engineering team through convenient working hours overlap.
05
Flexible Engagement Models
Whether you need to hire ML engineer talent for a specific model or build a complete ML team, choose an engagement model that fits your project and requirements. Scale your ML expertise up or down as your technical needs evolve.
06
Clean Code & Full Documentation
Get well-structured code, complete technical documentation and a clear handover so your team can maintain, scale and improve the solution with confidence. Every deliverable is structured to support smoother knowledge transfer and long-term maintenance.
Get in Touch
Talk to an AI Expert
Who We Serve
Industries We Serve
Fintech & Banking
Build fraud detection, credit scoring, risk assessment, and real-time transaction monitoring systems. Our engineers can develop models that analyze large volumes of financial data and help teams identify patterns, risks, and anomalies faster.
Healthcare
Develop clinical decision support, medical imaging, patient outcome prediction, and compliant ML solutions. Machine learning can help healthcare organizations process complex datasets and support faster, data-driven workflows.
Legal & Compliance
Automate document classification, contract analysis, compliance monitoring, and legal research workflows. Our engineers build ML systems that help teams process large document volumes and identify relevant information more efficiently.
E-commerce & Retail
Create recommendation engines, demand forecasting, churn prediction, and dynamic pricing solutions. ML models can analyze customer behavior, product data, and market patterns to deliver more personalized and responsive retail experiences.
HR & Recruitment
Build candidate matching, resume screening, workforce analytics, and talent prediction systems. These solutions can help recruitment teams process candidate data faster while supporting more efficient workforce planning.
SaaS & Technology
Add intelligent search, predictive analytics, ML-powered features and churn reduction capabilities to your products. Our engineers can integrate ML into existing platforms to create smarter and more personalized user experiences.
Manufacturing
Implement predictive maintenance, automated quality inspection, defect detection, and supply chain optimization. ML systems can analyze equipment, production, and operational data to identify issues early and improve process efficiency.
Real Estate
Develop property valuation, lead scoring, market prediction, and automated document processing solutions. Our engineers can use property and market data to support more informed decisions and streamline repetitive workflows.
Featured Projects
Success Stories That Transform Businesses
AI-powered LinkedIn outbound...
Legal Recruitment
Leadership Team
Meet Our
Founders
Shailesh Jain
Co-Founder · CMU Alumni
Nirav Shah
Co-Founder · Columbia, Ex UBS
Client Stories
What clients say about us.
“Quantal AI and Team are EXPERTS at building ANY AI functionality you’re seeking! We’ve hired them for 2 projects already — each completed ON TIME and UNDER BUDGET. Highly Recommended!”
Melissa C
myhomecarebiz.com“Working with Quantal AI team has been an absolute pleasure. Their technical aptitude is outstanding — they’re not only highly competent but also creative, thoughtful, and reliable. They built a complex integration for our wine business connecting PhotoRoom, Google Cloud, AWS, and Shopify, and it works beautifully.”
David F
Osteopathic Healing Hands“It was a pleasure working with Quantal AI team. Communication was smooth, deadlines were respected, and the overall collaboration was professional and efficient. I would definitely consider working together again in the future. Recommended!”
Ivana M
Elunic AG
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Read MoreFrequently Asked Questions
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What does a machine learning engineer do?When you hire machine learning engineer talent, you can bring in expertise to design, develop, train, deploy and maintain ML models and systems. They can also build data pipelines, optimize models, and monitor performance in production.
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What is the difference between a machine learning engineer and a data scientist?A data scientist focuses more on analyzing data, finding patterns, and developing models. A machine learning engineer focuses on building, deploying, scaling, and maintaining those models in production.
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What skills should a machine learning engineer have?A strong ML engineer should know Python, ML frameworks such as PyTorch or TensorFlow, data engineering, model evaluation, MLOps, cloud platforms and production deployment. Experience with NLP, computer vision, or LLMs can be valuable depending on the project.
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How quickly can I hire a machine learning engineer from Quantal AI?Quantal AI can match you with a suitable, vetted machine learning engineer within 24 hours, based on your project requirements and hiring model.
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What is the difference between an ML engineer and an AI engineer?An ML engineer primarily builds and operates machine learning models and systems. An AI engineer typically has a broader focus that can include ML, LLMs, generative AI, AI agents, and other intelligent applications.
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How much does it cost to hire a machine learning engineer?The cost depends on the engineer's experience, project complexity, engagement model, and duration. Quantal AI offers dedicated, hourly, and project-based options based on your requirements.
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Should I hire a dedicated ML engineer or work with an ML development company?Hire ML engineer talent when you need long-term ownership within your team or specialized support for a specific requirement. An ML development company is better suited when you need broader expertise, multiple specialists, or end-to-end project delivery.
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What ML frameworks and tools should an engineer know in 2026?Depending on the project, useful skills include PyTorch, TensorFlow, Scikit-learn, Hugging Face, MLflow, Docker, Kubernetes and cloud ML platforms such as AWS SageMaker, Google Vertex AI and Azure ML.
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How do I know if a machine learning engineer has real production experience?Look for experience deploying and monitoring models, building MLOps pipelines, handling model drift, managing production data, and solving real-world performance issues. Case studies and technical project discussions can also help verify practical experience.
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What happens after the ML model is deployed, and who maintains it?After deployment, the model needs monitoring, performance evaluation, drift detection, retraining, and ongoing optimization. Depending on your engagement model, Quantal AI engineers can continue maintaining and improving the ML system.
Hire Machine Learning Engineer Talent in 24 Hours
Tell us what you need to build, and we’ll match you with a machine learning engineer for hire within 24 hours based on your technical requirements.
Or email us directly at contact@quantaltech.ai or call +1 315 809 3225.