Gen AI Architect || On-site

In this role, we are seeking a highly skilled and experienced AI Engineer with hands-on expertise in Generative AI, AWS Bedrock, Azure Open AI, Document Intelligence tools, Huggingface, Agentic AI, and Advanced Data Pipelines. It s platforms semantic kernel, autogen, langchain.

Job Role & Responsibilities

• Architect and implement advanced generative AI models, focusing on applications such as image and text generation, image and document extraction, RAG, Prompt engineering, and synthetic data generation.
• Advanced Prompt Engineering techniques. Stay at the forefront of AI/ML and generative AI advancements, driving the adoption of new technologies and best practices within the team.
• Develop, fine-tune, and optimize generative AI models, ensuring they meet performance, scalability, and business objectives. Firm grasp on tools to compare outputs of each model and compare against each other.
• Apply solid data science principles to enhance AI/ML models, using statistical analysis and machine learning techniques to improve model accuracy and efficiency.
• Work closely with data engineers, software developers, and product managers to integrate generative AI models into products and services, aligning with business objectives.
• Ensure that AI models are designed and deployed with ethical considerations, adhering to industry standards and regulatory requirements. Qualifications we seek in you! Minimum

Skills required

• Bachelor s or Master s degree in computer science, AI, Machine Learning, Data Science, or a related field, with 5+ years of experience in AI/ML, particularly with generative AI models.
• Strong expertise in developing and deploying generative AI models. Proficiency in Python and experience with AI development libraries such as TensorFlow and PyTorch. Experience with Azure OpenAI, Open Source LLMs, and LangChain.
• Solid experience in applying data science principles, including statistical analysis, data preprocessing, and machine learning, to enhance AI/ML models.
• Familiarity with advanced techniques such as Retrieval-Augmented Generation (RAG), fine-tuning, prompt engineering, and zero-shot learning.
• Familiarity with NLP or computer vision related to generative AI applications