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Brief Job Description:
• Develop and deploy cutting-edge machine learning (ML) models, focusing on NLP, computer vision, and advanced deep learning techniques.
• Collaborate with business teams to gather requirements, ensuring AI/ML solutions align with
organizational objectives.
• Analyze large datasets to uncover patterns, trends, and actionable insights.
• Implement feature engineering, ensemble learning approaches, and fine-tuning of ML models for
optimal performance.
• Integrate and optimize AI/ML solutions using frameworks like TensorFlow, PyTorch, and Hugging Face.
• Leverage cloud platforms (AWS, Azure, or Google Cloud) to deploy scalable and efficient ML solutions.
• Stay abreast of advancements in AI/ML technologies, including LLMs, LangChain, and VLLM, to
incorporate innovative practices.
Educational Qualification: B.E or B.Tech
Experience: 4-6 years
Work Experience and Skills required:
• 4 to 6 years of overall experience in developing and deploying machine learning models.
• Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn.
• Expertise in NLP, embeddings, transformers, and libraries such as NLTK and Hugging Face
• Experience with advanced techniques like ensemble learning, feature engineering, and model fine
tuning
• Familiarity with cloud platforms (e.g., AWS, Azure, or Google Cloud) for deploying machine learning models.
• Strong background in data preprocessing, neural networks, and deep learning approaches.
• Excellent problem-solving and collaboration skills, with familiarity in domains like fintech, automation pipelines, and image processing.
Job ID: 147474469

Skills:
Retrieval-Augmented Generation (RAG), Sql, Python, Azure, Gcp, Microservices, Tensorflow, Pytorch, AWS, ML Frameworks, Generative AI Concepts, Agentic AI Frameworks, Cloud Platforms, Prompt Engineering, Vector Databases, Pinecone, Weaviate
Skills:
Computer Vision, Deep Learning, Tensorflow, Pandas, Numpy, Machine Learning, Opencv, Pytorch, Python, MediaPipe, ONNX, model optimization, Signal Processing, MIR Music Information Retrieval, visual data, Essentia, Librosa, Deployment, Statistics, time-series analysis, TorchScript, TensorRT
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