GEN AI Architect

Must have 15+ years of experience working in Data science, Machine learning and especially NLP technologies.
Strong Architecture exp is must.
Must have experience with Agentic AI frameworks LangGraph, LlamaIndex etc.
Strong understanding of Agents development.
Experience with Python programming language in 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

Able to apply deep learning and generative modeling techniques to develop LLM solutions in the field of Artificial Intelligence.

Utilize your extensive knowledge and expertise in machine learning (ML) with a focus on generative models, including but not limited to generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures.
Solid understanding of Model development, model serving, training/re-training techniques in a data sparse environment.
Very good understanding of Prompt engineering techniques in developing Instruction based LLMs.
Must be able to design, and implement state-of-the-art generative models for natural language processing (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 the customer needs.
Passionate to learn and stay updated with the latest advancements in generative AI and LLM.
Nice to have -contributions to the research community through publications, presentations, and participation in relevant conferences or workshops.
Evaluate and preprocess large-scale datasets, ensuring data quality and integrity, and develop data pipelines for training and evaluation of generative models.
Ability to articulate to business stakeholders on the hallucination effects and various model behavioral analysis techniques followed.
Exposure to developing Guardrails for LLMs both with open source and cloud native models.
Collaborate with software engineers to deploy and optimize generative models in production environments, considering factors such as scalability, efficiency, and real-time performance.
Nice to have- provide guidance to junior data scientists, sharing expertise and knowledge in generative AI and LLM, and contribute to the overall growth and success of the data science team.
Experience on Marklogic is good to have in it