AI is transforming every industry, but the gap between "we should use AI" and actually shipping AI-powered features is enormous. You need engineers who understand both the science and the engineering — people who can fine-tune a language model, build a reliable inference pipeline, and integrate it seamlessly into your product. CoreVision's AI/ML engineers have built recommendation engines at scale, deployed computer vision systems in production, and integrated LLMs into products used by millions. They bring battle-tested experience to your team.
Why Choose CoreVision for AI & Machine Learning
AI engineering is one of the most in-demand skills in tech, and the talent market is incredibly competitive. Senior AI/ML engineers command $300K+ salaries at FAANG companies, making them nearly impossible for most startups and mid-size companies to hire directly. CoreVision gives you access to this caliber of talent at a fraction of the cost and without the 6-month hiring timeline.
Our AI engineers don't just build models — they build products. They understand that a model is only as good as its integration, and they focus on delivering measurable business outcomes, not just impressive benchmarks.
- Production-focused — Engineers who have deployed ML models serving millions of predictions per day
- LLM specialists — Deep experience with GPT, Claude, Llama, and custom fine-tuning pipelines
- Full-stack AI — From data pipelines to model training to production deployment and monitoring
- Responsible AI — Built-in bias detection, explainability, and safety guardrails
Our AI & ML Development Process
AI projects require a different approach than traditional software development:
- Problem Definition — We start by understanding the business problem, not the technology. Many "AI" problems are better solved with simpler approaches, and we'll be honest about that.
- Data Assessment — We audit your data quality, volume, and accessibility to determine feasibility before writing a single line of model code.
- Rapid Prototyping — We build a proof-of-concept in 1–2 weeks so you can validate the approach before committing to full development.
- Model Development — Iterative training, evaluation, and optimization using state-of-the-art techniques and infrastructure.
- Production Deployment — We don't just hand over a notebook. We build production-grade inference pipelines with monitoring, A/B testing, and automated retraining.
Technologies We Master
Our AI/ML engineers work across the entire modern AI stack:
Large Language Models: OpenAI GPT-4, Anthropic Claude, Meta Llama, fine-tuning, RAG architectures, prompt engineering, and LangChain/LlamaIndex.
Machine Learning: TensorFlow, PyTorch, scikit-learn, XGBoost, and custom model architectures for classification, regression, and anomaly detection.
Computer Vision: Object detection, image segmentation, OCR, and video analysis using YOLO, SAM, and custom CNNs.
MLOps: MLflow, Weights & Biases, Kubeflow, feature stores, model registries, and automated retraining pipelines.
What You Get
- A senior AI/ML engineer with production deployment experience
- Feasibility assessment and honest recommendation on the right approach
- Working prototype within 1–2 weeks to validate before scaling
- Production-grade ML pipeline with monitoring and alerting
- Model performance dashboards and automated retraining
- Documentation and knowledge transfer for your team