Software Engineer(AI/ML)

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.