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Job details:
Total IT Exp: 6 to 8 Years
Relevant Exp: 5 to 6 Years
Education
- Postgraduate degree (Master’s or higher) in Computer Science, AI, Data Science, or relevant field.
Technical Skills
- Deep Learning: Proficient with Python and major frameworks (TensorFlow, PyTorch).
- LLMs: Hands-on with Large Language Models (GPT, BERT, Llama or similar); prompt engineering and fine-tuning.
-Vision: Advanced experience in image processing and CNN-based computer vision.
- Libraries/Tools:OpenCV, NumPy, Pandas, Scikit-learn.
- Data Analysis: Deep experience with large, complex structured and unstructured datasets.
- Cloud: Strong in GCP deployment of AI models; comfortable with Docker, Kubernetes, REST APIs.
- MLOps: Familiarity with MLOps tools for orchestration and monitoring.
Preferred Qualifications
- 6–8 years of relevant industry experience.
- Track record in industrial AI, automation, manufacturing, or quality inspection domains.
- Knowledge of edge AI, real-time inference, and multimodal systems.
Soft Skills
- Strong analytical, problem-solving, and data interpretation skills.
- Excellent written and verbal communication, capable of explaining AI concepts to varied audiences.
- Team player with a drive for continuous learning and technology innovation.
Nice-to-Have Skills
- MLOps pipeline automation.
- GPU optimization and inference acceleration (TensorRT, ONNX).
- Model compression, quantization, and edge deployment.
- Exposure to hyperspectral or optical imaging, or multi-language NLP.
Sample Use Cases
- AI-driven defect and anomaly detection (image and text).
- Automated quality inspection of products and documentation.
- Intelligent analytics combining vision and LLMs for manufacturing or industrial operations.
- Real-time computer vision and language monitoring for industrial automation.
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