AI Architect

NTT DATAPlano, TX
2h

About The Position

As a AI Architect with NTT DATA, you will be at the forefront of innovation, leveraging your skills to design and implement cutting-edge AI solutions. With a focus on creativity, collaboration, and technical excellence, this role offers a unique opportunity to work on transformative projects that drive business intelligence and decision-making across diverse industries.

Requirements

  • +5 year working with within full cycle deployment of AI technologies / legacy solutions.
  • +2 years working with OpenAI, LLaMA, Grok, Anthropic, Gemini, Bedrock, Coherent, Mistral; Hugging Face Transformers, TensorFlow.
  • +5 years in Python, SQL.
  • +4 years working with REACT, Angular, Streamlit, Chainlit, Flask.
  • +2 working with Langchain, LLaMA Index, Vector DBs like PineCone.
  • Ability to travel at least 25%.
  • Bachelor’s Degree required.

Nice To Haves

  • Preferred experience in Cognitive Services, NLP, GenAI, Agentic AI, ML.

Responsibilities

  • AI/ML Solution Development: Ideate, develop, and deploy AI/ML solutions focused on prediction, recommendation, text analytics, computer vision, natural language processing (NLP), and content intelligence.
  • Prototyping and PoCs: Rapidly develop prototypes and proof of concepts (PoCs) in areas such as LLMs, NLP, deep learning (DL), machine learning (ML), object detection, classification, and tracking.
  • Model Fine-tuning and Customization: Fine-tune and deploy distributed OpenAI models (e.g., DaVinci, Curie, Babbage, ADA) and apply prompt-engineering techniques to optimize model performance for specific use cases.
  • Agentic Workflows and RAG Applications: Develop applications using Retrieval-Augmented Generation (RAG) techniques and agentic workflows, enhancing AI solutions with advanced information retrieval and vector databases.
  • Cross-functional Collaboration: Work closely with cross-functional teams to design, build, and deploy AI solutions, ensuring alignment with client needs and project goals.
  • Continuous Improvement: Stay informed about the latest advancements in GenAI, machine learning, AI technologies, and cloud services to continuously optimize the technical stack.
  • Technical Communication: Clearly communicate complex technical concepts to non-technical audiences and provide training sessions to enhance data science skills within the organization.
  • Containerization and Deployment: Apply knowledge of containerization and deployment processes to ensure scalable and efficient AI solutions
  • Generative AI Integration: Utilize Azure OpenAI and other AI services to integrate Large Language Models (LLMs) into enterprise applications. Collaborate closely with business and IT teams to ensure successful implementation.
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