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).
- Solid 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.

📌 Software Engineer(AI/ML) (Secunderabad)
🏢 QUANTEON SOLUTIONS
📍 Secunderabad