AI/ML Engineering Senior Advisor

Overview

$140000
Full Time

Skills

Python
Git
Governance
MFC
Docker
Kubernetes
PyTorch
Deep Learning
Machine Learning
REST
Deployment
OCR
Computer Vision
Containerization
Segmentation
Workflow
Statistics
GCP

Job Details

Job Title : AI/ML Engineering Senior Advisor

Employment Type: Full-Time

Work Authorization Type: EAD

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

Work Location (City/State): Deerfield, IL (Hybrid - 3 days/week onsite)

Requirement Details

We're looking for a Senior/Lead-level candidate who can join the team and support our existing members with various architectural approaches. This role is more development-focused, so we're seeking someone with strong architectural Lead/hands-on experience.

What You Will Do

Work on ML Solution for RxQuality for MFC Project

Skills and Experience 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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