AI/ML Engineering Senior Advisor Computer Vision & LLMs, GenAI - Deerfield, IL (Hybrid 3 days/week onsite)

Overview

Hybrid
Depends on Experience
Full Time
No Travel Required

Skills

Generative Artificial Intelligence (AI)
Machine Learning (ML)
Python
PyTorch
Microsoft Azure
Artificial Intelligence
Cloud Computing
Continuous Integration
Google Cloud Platform
Amazon Web Services
MFC
Machine Learning Operations (ML Ops)
TensorFlow
OpenCV
Git
Docker
Kubernetes
Computer Vision

Job Details

Job Title: AI/ML Engineering Senior Advisor Computer Vision & LLMs, GenAI

Job Location: Deerfield, IL (Hybrid - 3 days/week onsite)

Fulltime

Duration: Long Term

Key Technology: Python, TensorFlow, PyTorch, Keras, OpenCV, DVC, MLflow, Git, CI/CD

Job Responsibilities:

  • Work on ML Solution for RxQuality for MFC Project

Skills and Experience Required:

Required:

  • 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 Google Cloud Platform 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.
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