AI and ML Engineers Ready to Build in Production
There is a significant gap between data scientists who run notebooks and ML engineers who ship production AI systems. Scadea places AI/ML engineers with verified experience taking models from experimentation through deployment, monitoring, and scale — the full lifecycle, not just the research phase.
AI/ML Engineering Capabilities
- Supervised, unsupervised, and reinforcement learning model development
- NLP, computer vision, and time-series modeling
- LLM fine-tuning, RAG architectures, and prompt engineering
- MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML
- Feature engineering, model evaluation, and drift monitoring
- Frameworks: PyTorch, TensorFlow, scikit-learn, Hugging Face
Engineering Discipline, Not Just Research
Our candidates bridge the gap between research and engineering. They know how to design model APIs, manage inference latency, implement A/B testing, and maintain models through data drift — production concerns that academic ML experience often misses. Many have operated in regulated industries where model explainability and auditability are mandatory.
From Individual Contributors to Team Leads
Whether you need a mid-level ML engineer to join an existing team or a senior AI architect to define your machine learning strategy, Scadea has the depth to match. We work with startups building their first AI product and enterprises scaling ML platforms across business units.
Ready to build something with AI? Contact Scadea to discuss your AI/ML engineering staffing need.