Sr. AI/ML Engineer

Qualifications

• 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)
• 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

Full Description

NAVA Software solutions is looking for a Sr. AI/ML EngineerDetails:AI/ML Engineer Senior AdvisorLocation: Chicago IL -Hybrid - 3 days/week onsiteDuration: 6-12 months

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 inComputer 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