Add genuine AI capability to your team. VSERV's vetted AI and ML engineers — from data scientists who build models to MLOps engineers who operationalise them — embed in your team and ship production-ready intelligence.
Most AI projects stall between the notebook and production. VSERV's AI / ML Engineer Augmentation gives you engineers who close that gap — building models that are accurate, robust, and deployable, with the MLOps infrastructure to keep them running.
From computer vision and NLP to recommendation systems and predictive analytics — our engineers embed in your team, work in your stack, and deliver AI that actually moves your product forward.
At a Glance
Six AI/ML capabilities that take models from experiment to production.
Supervised, unsupervised, and reinforcement learning with scikit-learn, TensorFlow, and PyTorch.
Text classification, entity extraction, sentiment analysis, and LLM-powered applications.
Image classification, object detection, OCR, and video analysis pipelines.
Training pipelines, experiment tracking (MLflow), and CI/CD for models — not just notebooks.
Fast, scalable model serving with TorchServe, FastAPI, or managed cloud AI services.
Forecasting, anomaly detection, and churn/risk models that drive business decisions.
From brief to models in production — a practical path.
Tell us your use case, data availability, and where AI fits in your product roadmap.
We propose engineers with the right model expertise and ML stack for your needs.
Access to your data, infrastructure, and repos — aligned on problem framing and success metrics.
Models trained, evaluated, deployed, and monitored in production.
Engineers who close the gap between AI experiment and production value.
Engineers who build models that run in production — not just Jupyter notebooks.
Structured MLOps practices that accelerate the build-evaluate-deploy cycle.
Bias detection, explainability, and fairness practices built into the development workflow.
Vetted for ML fundamentals, coding skill, and ability to communicate results clearly.
Common questions about hiring AI/ML engineers through VSERV staff augmentation.
scikit-learn, TensorFlow, PyTorch, and Keras — plus Hugging Face Transformers for NLP and LLM work.
Yes — prompt engineering, fine-tuning, RAG pipelines, and LLM application development with OpenAI and open-source models.
Yes — data preprocessing, feature engineering, model training, evaluation, and production deployment.
MLflow, DVC, Weights & Biases, Kubeflow, and cloud ML services (SageMaker, Azure ML, Vertex AI).
Yes. Model serving via FastAPI or managed services — integrated into your existing product architecture.
Typically within 3–5 business days of a confirmed engagement.
Talk to VSERV about AI / ML Engineer Augmentation and embed production AI expertise in your team within days.