GEN AI Architect

Exp : 15Yrs to 20yrs

Primary skill :- GEN AI Architect, Building GEN AI solutions, Coding, AI ML background, Data engineering, Azure or AWS cloud.

The Generative Solutions Architect will be responsible for designing and implementing cutting-edge generative AI models and systems. He / She will collaborate with data scientists, engineers, product managers, and other stakeholders to develop innovative AI solutions for various applications including natural language processing (NLP), computer vision, and multimodal learning. This role requires a deep understanding of AI/ML theory, architecture design, and hands-on expertise with the latest generative models.

Key Responsibilities

• GenAI application conceptualization and design: Understand the use cases under consideration, conceptualization of the application flow, understanding the constraints and designing accordingly to get the most optimized results. Deep knowledge to work on developing and implementing applications using Retrieval-Augmented Generation (RAG)-based models, which combine the power of large language models (LLMs) with information retrieval techniques.
• Prompt Engineering: Be adept at prompt engineering and its various nuances like one-shot, few shot, chain of thoughts etc and have hands on knowledge of implementing agentic workflow and be aware of agentic AI concepts
• NLP and Language Model Integration - Apply advanced NLP techniques to preprocess, analyze, and extract meaningful information from large textual datasets. Integrate and leverage large language models such as LLaMA2/3, Mistral or similar offline LLM models to address project-specific goals.
• Small LLMs / Tiny LLMs: Familiarity and understanding of usage of SLMs / Tiny LLMs like phi3, OpenELM etc and their performance characteristics and usage requirements and nuances of how they can be consumed by use case applications.
• Collaboration with Interdisciplinary Teams - Collaborate with cross-functional teams, including linguists, developers, and subject matter experts, to ensure seamless integration of language models into the project workflow.
• Text / Code Generation and Creative Applications - Explore creative applications of large language models, including text / code generation, summarization, and context-aware responses.

Skills & Tools

• Programming Languages -

  Proficiency in Python for data analysis, statistical modeling, and machine learning.

• Machine Learning Libraries - Hands-on experience with machine learning libraries such as scikit-learn, Huggingface, TensorFlow, and PyTorch.

• Statistical Analysis - Strong understanding of statistical techniques and their application in data analysis.

• Data Manipulation and Analysis - Expertise in data manipulation and analysis using Pandas and NumPy.

• Database Technologies - Familiarity with vector databases like ChromaDB, Pinecone etc, SQL and Non-SQL databases and experience working with relational and non-relational databases.

• Data Visualization Tools - Proficient in data visualization tools such as Tableau, Matplotlib, or Seaborn.

• Familiarity with cloud platforms (AWS, Google Cloud, Azure) for model deployment and scaling.

• Communication Skills - Excellent communication skills with the ability to convey technical concepts to non-technical audiences.