Hire Expert Vertex AI Developers
Production ML systems for established businesses, not proof-of-concept demos. Our Vertex AI engineers have shipped AutoML pipelines, generative AI applications, and MLOps infrastructure for clients in the US, UK, Ireland, and Australia.
In short
Vertex AI is Google Cloud's unified ML platform for building, training, and deploying machine learning models at scale, covering AutoML, custom model training, generative AI, and MLOps pipelines. RaftLabs has used Vertex AI to ship production systems for clients in retail, healthcare, and fintech across the US, UK, Australia, and Ireland, including an AI-OCR loyalty platform that processed 1,000+ receipts per week from day one. Our team handles the full lifecycle from data pipeline design through model deployment and ongoing monitoring, so clients get working systems rather than proof-of-concept demos.
Features of Vertex AI
Custom Model Training with AutoML
Vertex AI lets you build and train machine learning models matched to your data, even with minimal coding. AutoML handles model selection and optimization, cutting development time without sacrificing accuracy.
Managed Model Deployment
Deploy models at scale without managing infrastructure. Vertex AI handles serving, scaling, and load balancing so models perform reliably in production under variable traffic.
Pre-trained Models via Model Garden
Access a library of pre-trained models for image recognition, natural language processing, and translation, reducing time-to-value on common tasks by weeks.
Generative AI with Foundation Models
Build generative AI applications using advanced foundation models, enabling capabilities like content creation, chatbots, and recommendation systems at enterprise scale.
MLOps and Model Management Tools
Vertex AI pairs with MLOps tooling for monitoring, versioning, and governance, so teams can maintain model quality and operational efficiency after launch, not just at launch.
Unified ML Environment
Data preparation, training, evaluation, deployment, and monitoring live in one platform, simplifying the machine learning workflow from raw data to production inference.
Top 5 use cases of Vertex AI
IrelandWhy choose our Vertex AI experts?
1. Deep Google Cloud ML Expertise
Our engineers have extensive hands-on experience building, training, and deploying custom machine learning models on Google Cloud. They work across AutoML and pre-trained models from Model Garden, which means faster time-to-production and more efficient AI systems for your use case.
2. Generative AI and Advanced Solutions
The team is proficient in generative AI, building chatbots, content generation tools, and personalized recommendation systems that are practical and aligned with measurable business goals, not experimental demos.
3. Scalable Deployment and Infrastructure
Our engineers manage AI deployments at scale with production-ready infrastructure. They optimize model serving and load configuration so AI systems work reliably in real-world environments under variable traffic.
4. MLOps and Operational Excellence
By applying MLOps best practices, our engineers monitor, version, and govern models throughout their operational lifecycle. This keeps accuracy, efficiency, and compliance in check long after initial launch.
5. Business-Focused AI Solutions
Our Vertex AI engineers translate technical capability into measurable outcomes, cost reduction, throughput gains, accuracy targets. They adapt solutions to specific industry constraints rather than mapping generic architectures onto your problem.
RaftLabs vs in-house vs freelancers
| RaftLabs | In-House | Freelance | |
|---|---|---|---|
| Time to hire top Vertex AI developers | 1 day to 2 weeks | 4 to 6 weeks | 1 to 12 weeks |
| Project initiation time | 1 day to 2 weeks | 2 to 10 weeks | 1 to 10 weeks |
| Risk of project failure | Exceptionally low with a 98% success rate | Low | Very High |
| Developers supported by project management | Yes, dedicated PM and Agile processes | Varies | No |
| Exclusive development team | Yes, dedicated team guaranteed | Yes | No |
| Assurance of work quality | Yes, with quality assurance processes | Yes | Varies |
| Advanced development tools and workspace | Yes, enterprise-grade tools | Yes | Varies |
Dedicated senior engineers from $6,000/month (minimum pod $12,000/month).
What powers our products
Industries we serve
- 01
Healthcare App Development
HIPAA-aware EHR integrations, patient portals, and clinical workflow tools for digital health startups and multi-location practices.
- 02
Fintech Software Development Company
Payment rails, neobank backends, lending origination, and RegTech compliance systems. Fixed-cost delivery for fintech startups.
- 03
Hospitality Software Development Company
Property management systems, direct booking engines, guest apps, and channel management for hotels, resorts, and serviced apartment groups.
- 04
Ecommerce Software Development Company
Custom ecommerce software for online retailers, B2B sellers, and marketplace operators, platform development, marketplace, headless commerce, AI personalisation, subscription, and B2B portals.
- 05
EdTech Software Development Company
Learning platforms, LMS, AI tutors, and school management systems for ed-startups and institutions scaling past generic tools.
- 06
B2B SaaS Development
B2B SaaS products, marketplaces, and creator platforms. Built end to end.
- 07
Loyalty Apps
Loyalty is about more than points, it's about building deeper engagement. When customers don't feel connected, retention drops. We've built systems like Aldifest, Instantor, and Energia Rewards that keep users coming back with personalized referrals, rewards, and multilingual experiences. These platforms make user experience simple and drive real business growth.
- 08
Media & Communication
We build production-ready media and communication apps: live streaming, on-demand content, social audio networks, and real-time video infrastructure. Delivered on time, built to handle real user loads.
FAQs
Timeline depends on project complexity. A simple proof-of-concept with AutoML can be delivered in 3-4 weeks. Standard custom ML solutions typically take 8-12 weeks from discovery to production deployment. Complex enterprise systems with multiple models and integrations may require 4-6 months. We provide detailed timelines during the discovery phase based on your specific requirements.
Yes, we have practical experience migrating models from various platforms and legacy systems to Vertex AI. The process involves assessing the model, redesigning the architecture for Vertex AI, migrating data pipelines, retraining if necessary, and testing for performance parity. Migration projects generally take 6-10 weeks, depending on model complexity and data volume.
We work with organizations of all sizes, startups, SMBs, and Fortune 500 companies. We offer flexible engagement models to fit different budgets and requirements. Startups benefit from rapid POC development and cost-effective approaches, while enterprises get the depth needed for large-scale, mission-critical deployments.
Yes, Vertex AI provides well-documented APIs and direct integration capabilities. We can connect to your existing databases (SQL, NoSQL), data warehouses (BigQuery, Snowflake, Redshift), BI tools, CRM systems, and custom applications. We have integrated Vertex AI models with Salesforce, SAP, custom web and mobile apps, and legacy systems. Integration is part of our standard delivery process.
We start every project by defining clear, measurable success metrics aligned with your business goals. These might include revenue increase, cost reduction, efficiency gains, or customer satisfaction improvements. We implement tracking and analytics to monitor these metrics continuously.