Sr. Engineer - Artificial Intelligence

Job Summary:

The incumbent will work on real-world projects, helping design, develop, and deploy AI models and data-driven solutions. You will play a crucial role in helping businesses make informed decisions by collaborating with stakeholders, designing data models, creating algorithms, and sharing meaningful insights to drive use case success.

Key Responsibilities:

• AI Solution Development:

• Design, train, fine-tune, and evaluate generative models (e.g., GPT, LLaMA, Stable Diffusion, DALLE, etc.).
• Collaborate with cross-functional teams including product, research, and engineering to integrate GenAI capabilities into products.
• Develop a platform approach, optimize models for performance, latency, and cost in production environments.

• Model Integration:

• Integrate foundational models and retrieval-augmented generation (RAG) techniques.

• Backend Development:

• Implement backend services and API endpoints to support AI solutions.

• Frontend Integration:

• Develop or adapt frontend interfaces for user interaction.
• Mentor junior engineers and contribute to best practices in model development and deployment.
• Ensure ethical and responsible AI practices in model design and usage.

Person Profile:

Qualification:

Bachelor's or Master's degree in Computer Science, Machine Learning, Mathematics / Statistics, or a related field.

Experience / Must Have:

• 35+ years of experience in machine learning, with at least 2 years focused on generative AI.
• Preferably with GenAI use cases in Chemical, Pharma, or Manufacturing industries.
• Highly agile individuals who would like to make an impact in a global setup.
• Proficiency in Python for data handling and AI-related scripting.
• Familiarity with data pipeline development, ETL processes, and data preprocessing techniques.
• Knowledge of testing frameworks and tools to ensure model robustness, as well as experience with automated testing for QA processes.
• Familiarity with Retrieval-Augmented Generation techniques and their applications.
• Knowledge of prompt engineering techniques to optimize generative AI models for specific tasks and enhance output relevance.
• Experience in predictive modeling areas, such as traditional supervised and unsupervised learning.
• Basic familiarity with foundational generative AI models and Natural Language Processing (NLP).