AI Architect

NTT DATA•Plano, TX

About The Position

Possess deep understanding of cloud systems, network architectures, identity management (authentication and RBAC), and various services available in cloud systems. Experience working on all aspects of enterprise scale implementation for AI/GenAI solutions including architecture, design, security, infrastructure, MLOps/GenAIOps Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn. Leverage advanced knowledge of Python open-source software stack such as Django or Flask, Django Rest or FastAPI, etc. Deep knowledge in statistics and Machine Learning models, deep learning models, NLP, Generative Adversarial Networks (GAN), and other generative models. Experience working with RAG technologies and LLM frameworks, LLM model registries (Hugging Face), LLM APIs, embedding models, and vector databases Employ technical knowledge and hands-on experience with Azure OpenAI, Google Vertex Gen AI, and AWS LLM foundational models, BERT, Transformers, PaLM, Bard, etc. Have problem-solving abilities combined with good communication skills. Deploy knowledge of Azure DevOps, YAML, Shell scripting, Terraform, and pipelines. Understand how to fine-tune models, quantize models, and deploy models. Design integrations between various cloud-based services through APIs, keeping modular designs in mind. Apply knowledge of data engineering tools and technologies and understanding of concepts such as big data and data pipelines Scope, manage, and drive complex GenAI projects and programs to successful completion. Design and implement GenAI frameworks and patterns tailored to client needs. Architect and lead the implementation of GenAI use cases, projects, and POCs across multiple industries. Work on RAG models and Agents Frameworks to enhance GenAI solutions by incorporating relevant information retrieval mechanisms and frameworks Utilize best practices and creativity to address challenges and deliver impactful solutions. Conduct market research, formulate perspectives, and communicate insights to clients and stakeholders. Establish strong client relationships, gaining insights into project requirements and challenges. Communicate complex technical concepts clearly to non-technical audiences. Handle data sets of varying complexity, processing massive data streams in distributed computing environments. Apply business acumen to analyze data, develop insightful reports, and solve problems. Perform ad hoc analyses based on evolving business needs. Participate in the analysis and resolution of issues related to information flow and data content. Collaborate with data stakeholders to address challenges and enhance data quality. Mentor junior data scientists and GenAI engineers, fostering professional growth within the team. Conduct training sessions to enhance overall data science skills within the organization.

Requirements

Deep understanding of cloud systems, network architectures, identity management (authentication and RBAC), and various services available in cloud systems.
Experience working on all aspects of enterprise scale implementation for AI/GenAI solutions including architecture, design, security, infrastructure, MLOps/GenAIOps
Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn.
Leverage advanced knowledge of Python open-source software stack such as Django or Flask, Django Rest or FastAPI, etc.
Deep knowledge in statistics and Machine Learning models, deep learning models, NLP, Generative Adversarial Networks (GAN), and other generative models.
Experience working with RAG technologies and LLM frameworks, LLM model registries (Hugging Face), LLM APIs, embedding models, and vector databases
Employ technical knowledge and hands-on experience with Azure OpenAI, Google Vertex Gen AI, and AWS LLM foundational models, BERT, Transformers, PaLM, Bard, etc.
Have problem-solving abilities combined with good communication skills.
Deploy knowledge of Azure DevOps, YAML, Shell scripting, Terraform, and pipelines.
Understand how to fine-tune models, quantize models, and deploy models.
Design integrations between various cloud-based services through APIs, keeping modular designs in mind.
Apply knowledge of data engineering tools and technologies and understanding of concepts such as big data and data pipelines
2+ years of experience architecting high-impact GenAI / Agentic solutions for diverse clients.
12+ years of experience participating in projects that focused on one or more of the following areas: Predictive Analytics, Data Design, Generative AI, AI/ML, ML Ops
3+ years of experience using Python.
Ability to travel at least 50% when required.

Responsibilities

Scope, manage, and drive complex GenAI projects and programs to successful completion.
Design and implement GenAI frameworks and patterns tailored to client needs.
Architect and lead the implementation of GenAI use cases, projects, and POCs across multiple industries.
Work on RAG models and Agents Frameworks to enhance GenAI solutions by incorporating relevant information retrieval mechanisms and frameworks
Utilize best practices and creativity to address challenges and deliver impactful solutions.
Conduct market research, formulate perspectives, and communicate insights to clients and stakeholders.
Establish strong client relationships, gaining insights into project requirements and challenges.
Communicate complex technical concepts clearly to non-technical audiences.
Handle data sets of varying complexity, processing massive data streams in distributed computing environments.
Apply business acumen to analyze data, develop insightful reports, and solve problems.
Perform ad hoc analyses based on evolving business needs.
Participate in the analysis and resolution of issues related to information flow and data content.
Collaborate with data stakeholders to address challenges and enhance data quality.
Mentor junior data scientists and GenAI engineers, fostering professional growth within the team.
Conduct training sessions to enhance overall data science skills within the organization.