Description

Design, train, and deploy advanced machine learning and computer vision systems.

This role is hybrid.

Responsibilities

• Develop and fine-tune deep learning models for object detection, segmentation, and OCR tasks.
• Build and serve REST APIs for model inference using TF Serving, TorchServe, or FastAPI.
• Implement MLOps workflows using DVC, MLflow, and CI/CD pipelines.
• Architect Agentic AI and RAG pipelines to integrate LLMs into enterprise systems.
• Deploy containerized models using Docker and Kubernetes within cloud environments.

Required Skills

• 7+ years of hands-on experience in applied machine learning and AI system deployment.
• Strong Python engineering background.
• Expertise with TensorFlow, PyTorch, Keras, and OpenCV.
• Experience training YOLOv5/v8, EfficientNet, Faster-RCNN, TrOCR, and Vision Transformers (ViT).
• Proficiency with LLMs including GPT-4, LLaMA, and Claude.
• Practical knowledge of LangChain, CrewAI, or AutoGPT for Agentic AI architectures.
• Experience with DVC, MLflow, Git, and containerization (Docker/Kubernetes).
• Solid understanding of statistics, optimization, and deep learning principles.
• Knowledge of AI governance, fairness, and model explainability.

Preferred Skills

• Cloud deployment experience, specifically with Azure.

Key Skills

Education

Any Graduate