LiveHybridFull-timeApply by 1 Nov 2026
AI/ML Engineering Senior Advisor
Deerfield, United States
- Experience
- 8–30 years
- Employment
- Full-time
- Work mode
- Hybrid
- Salary
- Not disclosed
- Deadline
- Apply by 1 Nov 2026
- Posted
- 2025-08-18
Required skills
| Skill | Experience | Level |
|---|---|---|
| Python | 7+ years | Not specified |
| Machine Learning | 7+ years | Not specified |
| TensorFlow | 6+ years | Expert |
| Keras | 4+ years | Advanced |
| OpenCV | 5+ years | Advanced |
| GIT | 6+ years | Advanced |
| CI/CD | 6+ years | Advanced |
| MLOPS | 4+ years | Advanced |
| Pytorch | 4+ years | Advanced |
| Computer Vision | 4+ years | Advanced |
| RESTful API | 3+ years | Advanced |
| AWS | 3+ years | Intermediate |
| Deep Learning | 3+ years | Advanced |
| Google Cloud Platform - IAAS | 1+ years | Beginner |
| Artificial Intelligence | 5+ years | Not specified |
About the role
Job Description:
- 7+ years of hands-on experience in applied machine learning, deep learning, and AI system deployment
- Strong Python engineering background with ML/DL frameworks: TensorFlow, PyTorch, Keras, OpenCV
- Proven experience in Computer Vision tasks, including object detection, segmentation, and OCR
- Experience training and fine-tuning models such as: YOLOv5/v8, EfficientNet, Faster-RCNN, TrOCR, Vision Transformers (ViT)
- Practical experience building and serving REST APIs for inference (TF Serving, TorchServe, FastAPI)
- Hands-on with MLOps tools: DVC, MLflow, Git, CI/CD, containerization (Docker/Kubernetes)
- Cloud deployment experience (Azure preferred; AWS or GCP acceptable)
- LLM/GenAI experience: building, fine-tuning, or prompting models such as GPT-4, LLaMA, Claude, etc.
- Familiarity with RAG (Retrieval-Augmented Generation) pipelines and integration into enterprise systems
- Understanding of Agentic AI architectures (e.g., LangChain, CrewAI, AutoGPT) for orchestrated task agents or workflow automation
- Strong foundations in statistics, optimization, and deep learning principles
- Clear understanding of AI governance, fairness, and model explainability