Software Engineer(AI/ML)

2 days ago

Hyderabad, India Quanteon Solutions LLP Full time

Job Description Key Responsibilities: - Design, Develop, and Deploy ML Models: - Create ML models tailored for real-world applications, focusing on solving business or technical challenges. - Design solutions that are scalable and production-ready, ensuring models are integrated seamlessly into existing systems. - Work with Large Datasets: - Handle large volumes of data, performing data preprocessing to clean and prepare datasets for model training. - Conduct feature engineering to improve the quality and relevance of data used in model building. - Model Evaluation: - Evaluate model performance using appropriate metrics, ensuring they meet business and technical requirements. - Continuously optimize models to improve accuracy, efficiency, and robustness. - Collaborate with Cross-Functional Teams: - Work with data scientists, engineers, and other stakeholders to integrate ML solutions into the product pipeline and production systems. - Ensure that models align with business goals and technical requirements. - Stay Up-to-Date with ML/AI Research: - Keep track of the latest trends, papers, and technological advancements in the ML/AI field. - Apply cutting-edge techniques like Generative AI, Large Language Models (LLMs), and Retrieval Augmented Generation (RAG) to real-world problems. Skills & Experience: - Solid Experience with Python: - Python is a core language in this role, with strong experience required for building and deploying ML models. - Familiarity with Python-based ML libraries such as scikit-learn, TensorFlow, and PyTorch. - Machine Learning Libraries: - scikit-learn for traditional ML algorithms. - TensorFlow and PyTorch for deep learning applications, particularly if working with neural networks or large-scale AI systems. - Data Pipelines & Model Deployment: - Experience in building and maintaining data pipelines to manage data flow and prepare data for model training. - Proficiency in deploying models into production, ensuring their scalability and performance in real-world environments. - Performance Tuning: - Optimize models for efficiency, fine-tuning hyperparameters and addressing any overfitting or underfitting issues. - Implementing techniques like regularization, batch normalization, or dropout for improving deep learning models. - Cloud Platforms (AWS/GCP/Azure): - Familiarity with cloud services to deploy, scale, and manage ML models and data pipelines (e.g., AWS SageMaker, GCP AI, Azure ML). - Strong Problem-Solving & Analytical Skills: - Ability to break down complex problems into manageable pieces and apply the best techniques to solve them. - Solid background in statistical analysis and optimization. - Communication & Teamwork: - Excellent written and verbal communication skills to articulate complex technical solutions to stakeholders. - Collaborate effectively with cross-functional teams to integrate ML solutions into production. Preferred Experience: - Generative AI & LLM Tuning: - Hands-on experience working with Generative AI models, like GPT-3 or similar LLMs. - Familiarity with Retrieval Augmented Generation (RAG) to enhance the ability of LLMs to generate accurate, context-aware responses. - Deep Learning: - Strong understanding of deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. - Implementing these models for tasks like image recognition, text processing, or language understanding.

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