Top 1% Talent
Rigorous Technical Screening
Only 1 in 100 applicants passes our strict technical screening for both algorithmic math and backend engineering — the same bar behind every hire ai talent engagement we staff.
Hire AI/ML Engineers for
Machine Learning & Generative AI
We connect you with vetted AI and machine learning engineers who build, train, and deploy generative AI and ML systems — from LLM pipelines to production-grade model infrastructure.
*No pressure. No obligation. Just honest product insights from our experts.
Zero-Risk 1-Week Free Trial
AI-Empowered AI/ML Engineers To Code Fast And Smart
In 48Hr Build Your Team
Stop Experimenting. Hire Production-Ready
AI Engineers.
The AI hype cycle has flooded the market with theoretical data scientists who know how to train a model but have no idea how to deploy it into a live software environment securely. This is exactly why serious teams hire ai developers through a vetted partner instead of gambling on a random freelance profile they found on a job board.
Because VGD Technologies operates its own AI innovation lab (VGD NEXT), our talent pool for teams who hire machine learning engineers and hire generative ai engineers is fundamentally different — hardcore software engineers who have mastered ML, not theorists who only build models in a notebook. They architect secure API layers, optimize inference costs, and deploy AI directly into your live web and mobile applications. Need a narrower LLM specialist? Our Generative AI & LLM team covers that.
Production-Ready Focus
MLOps & Scalability
Our devs understand MLOps. They build AI that's scalable, secure, and optimized for cloud compute costs (AWS SageMaker, Azure ML), so you get production infrastructure, not just model research.
Timezone Aligned
Seamless Daily Collaboration
Our engineers overlap with US, UK, AUS, and UAE business hours for seamless daily collaboration, whether you hire from our global bench or a dedicated pod based in India.
100% IP & Data Protection
Enterprise Security
Your proprietary data is your most valuable asset. We enforce strict NDAs and Zero-Trust data architecture from day one, for every hire ai engineers engagement, no exceptions.
Hire Specialists for the Modern AI Ecosystem
Hire Specialists for the Modern
AI Ecosystem
Hire Generative AI & LLM Developers
Build the next generation of intelligent tools. Hire experts proficient in OpenAI, Anthropic (Claude), and open-source models (Llama 3) — the same specialization covered by our dedicated Generative AI & LLM division. Whether you hire generative ai engineers for a new product or a single llm developer to build custom RAG pipelines that securely answer questions from your own data, this is exactly where that work lives.
Hire Machine Learning (ML) Engineers
Turn raw data into predictive power. Hire Python specialists who utilize TensorFlow and PyTorch to build custom recommendation engines, dynamic pricing algorithms, and churn-prediction models that directly increase your ROI. Whether you hire machine learning engineers for a single high-stakes model or hire ml developers to staff an entire predictive analytics team, every engineer ships production code, not a notebook that never leaves a laptop.
Hire Computer Vision & NLP Experts
Give your software eyes and ears. Augment your team with engineers who build automated image processing workflows (OpenCV), visual quality assurance, and advanced Natural Language Processing (NLP) — the same domain expertise behind our dedicated Core Machine Learning practice. These are specialized artificial intelligence developers, not generalists stretching outside their actual depth of experience.
Hire MLOps & Data Engineers
Deploy and scale without crashing. Hire infrastructure experts who build robust data pipelines (ETL) and deploy AI models using Docker and Kubernetes to ensure sub-second inference times. This is where you hire dedicated machine learning developers who think in production uptime and cost-per-inference, not just training accuracy on a benchmark dataset that never sees real traffic.
Hire Generative AI & LLM Developers
Build the next generation of intelligent tools. Hire experts proficient in OpenAI, Anthropic (Claude), and open-source models (Llama 3) — the same specialization covered by our dedicated Generative AI & LLM division. Whether you hire generative ai engineers for a new product or a single llm developer to build custom RAG pipelines that securely answer questions from your own data, this is exactly where that work lives.
Hire Machine Learning (ML) Engineers
Turn raw data into predictive power. Hire Python specialists who utilize TensorFlow and PyTorch to build custom recommendation engines, dynamic pricing algorithms, and churn-prediction models that directly increase your ROI. Whether you hire machine learning engineers for a single high-stakes model or hire ml developers to staff an entire predictive analytics team, every engineer ships production code, not a notebook that never leaves a laptop.
Hire Computer Vision & NLP Experts
Give your software eyes and ears. Augment your team with engineers who build automated image processing workflows (OpenCV), visual quality assurance, and advanced Natural Language Processing (NLP) — the same domain expertise behind our dedicated Core Machine Learning practice. These are specialized artificial intelligence developers, not generalists stretching outside their actual depth of experience.
Hire MLOps & Data Engineers
Deploy and scale without crashing. Hire infrastructure experts who build robust data pipelines (ETL) and deploy AI models using Docker and Kubernetes to ensure sub-second inference times. This is where you hire dedicated machine learning developers who think in production uptime and cost-per-inference, not just training accuracy on a benchmark dataset that never sees real traffic.
Engagement models designedFor your scale
Monthly Contract Basis
- 160 Hours / Dedicated Focus
- 1-Week Free Trial Eligible
Hourly Basis
- Scale Hours Up Or Down
- Detailed Timesheets
Partial / Quick Tasks
- Emergency Bug Fixes
- 1-2 Day Requirement
The VGD Zero-RiskGuarantee: 1-Week Free Trial
We let our code do the talking. Sign a contract of 3 months or more, and your first week is completely on us. If you aren't 100% thrilled with their AI logic, architectural knowledge, and communication after 7 days, walk away and pay nothing.
How to hire
(The 48-hour process)
- 1.Share Requirements
- 2.Resume Shortlist
- 3.Interview
- 4.Onboard
Frequently Asked Questions
An ML engineer focuses specifically on building, training, and deploying machine learning models — deep expertise in algorithms, data pipelines, and model operationalization. An AI engineer has a broader scope that often includes ML plus computer vision, NLP, generative AI, and agentic workflow integration. In practice, many job descriptions use the terms interchangeably, which is why defining the exact skillset you need is the first step before hiring either.
Hiring AI developers starts with defining whether you need model-building expertise (ML engineer), applied AI integration (AI engineer), or generative AI/LLM specialization — each requires a different skill profile. The fastest low-risk path is a company offering pre-vetted, dedicated AI developers who can start within days rather than the 4-6 months typical of direct full-time hiring.
Senior AI/ML engineer compensation in the US typically runs $200,000-$350,000+ total, while mid-level roles range $150,000-$220,000. Dedicated development partners in nearshore or offshore markets offer comparable talent at 40-60% lower cost, without the multi-month direct-hire recruiting cycle.
Direct hiring for senior AI/ML roles typically takes 4-6 months from opening a role to accepted offer, including sourcing, interviewing, and negotiating notice periods. Partnering with a dedicated AI development team compresses this dramatically to 2-4 weeks, since pre-vetted engineers are already available rather than being sourced from scratch.
A generative AI engineer specializes in large language models — fine-tuning, prompt engineering, and RAG (Retrieval-Augmented Generation) architecture — rather than traditional predictive ML models. If your project involves building a custom AI product around LLMs, our Generative AI & LLM development team covers exactly this specialization, separate from standard AI/ML engineering hires.
Full-time hires make sense when you're building long-term core AI capability inside your product. A dedicated or contract team works better for project-based work, filling a specific skill gap, or when you need to start quickly without a multi-month hiring cycle. Many companies use a hybrid model — a small full-time core team supplemented by dedicated specialists during high-demand phases.
An LLM developer focuses specifically on working with large language models — integrating APIs, building RAG pipelines, and fine-tuning models on proprietary data — a narrower specialization within generative AI. For most product teams, this role overlaps with custom LLM development work rather than being hired as a fully separate function.
Yes — computer vision and NLP are specialized ML sub-disciplines, so it's worth confirming a candidate's specific project history in that area rather than assuming general ML experience transfers directly. Our Core Machine Learning team works across computer vision, NLP, and predictive analytics, so we match engineers to the specific technical domain your project needs.
Go beyond coding tests — ask candidates to walk through a real AI project they've shipped, including what evaluation metrics they used and how they handled production failures, not just notebook prototypes. For LLM-specific roles, ask about RAG architecture decisions and how they'd prevent hallucinations; weak candidates typically can only describe successes, not what went wrong and how they fixed it.
Yes — this requires engineers experienced specifically in agentic architecture, where an AI system doesn't just respond to a single prompt but autonomously calls tools, checks conditions, and executes multi-step tasks. This is a more advanced skillset than standard model integration, since it involves orchestrating decision logic across an entire workflow rather than a single input-output exchange.
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