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.

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 Qualifications / Skills:

• Bachelors or Masters 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.

Preferred Qualifications/ Skills

• 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.