GEN AI Architect

Overview

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Must have 15+ years of experience working in Data science, Machine learning and especially NLP technologies. Strong architecture exp is a must. Must have experience with Agentic AI frameworks LangGraph, LlamaIndex etc. Strong understanding of Agents development. Experience with Python programming language is a must. Ability to develop Python code as needed and train the Developers based on business needs. Experience with AWS / Azure ecosystem is a must. Preferable candidate should possess Pharma background in Clinical or Regulatory processes. Strong understanding of cloud native application development in AWS / Azure. Exposure to various LLM technologies and solid understanding of Transformer Encoder Networks.

Essential job tasks and responsibilities are described below.

Responsibilities

• Able to apply deep learning and generative modeling techniques to develop LLM solutions in the field of Artificial Intelligence.
• Utilize extensive knowledge in machine learning (ML) with a focus on generative models, including GANs, VAEs, and transformer-based architectures.
• Understand model development, model serving, and training / re-training techniques in a data-sparse environment.
• Apply prompt engineering techniques in developing Instruction-based LLMs.
• Design and implement state-of-the-art generative models for NLP tasks such as text generation, text completion, language translation, and document summarization.
• Work with SAs and collaborate with cross-functional teams to identify business requirements and deliver solutions that meet customer needs.
• Stay updated with the latest advancements in generative AI and LLMs.
• Evaluate and preprocess large-scale datasets, ensure data quality and integrity, and develop data pipelines for training and evaluation of generative models.
• Articulate to business stakeholders on hallucination effects and behavior analysis techniques.
• Expose and develop guardrails for LLMs using open source and cloud-native models.
• Collaborate with software engineers to deploy and optimize generative models in production, considering scalability, efficiency, and real-time performance.
• Provide guidance to junior data scientists and contribute to the growth of the data science team.
• Experience with MarkLogic is a plus.

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