Principal Data and Agentic Architect , Professional Services

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Solutions Architect

This role focuses on designing and implementing AWS solutions, with a strong emphasis on AI and ML, particularly Large Language Models (LLMs) and Generative AI. The role involves acting as a trusted advisor to customers, guiding them through their cloud journey and leveraging AWS services to meet their business objectives. It requires expertise in ML/AI fundamentals, training/inference lifecycles, and optimization, with experience in platforms like vLLM or TensorRT being preferred. The role also touches on inference infrastructure and serving.

What you'd actually do

  1. Designing and implementing complex, scalable, and secure AWS solutions tailored to customer needs
  2. Providing technical guidance and troubleshooting support throughout project delivery
  3. Collaborating with stakeholders to gather requirements and propose effective migration strategies
  4. Acting as a trusted advisor to customers on industry trends and emerging technologies
  5. Sharing knowledge within the organization through mentoring, training, and creating reusable artifacts

Skills

Required

  • Bachelor's degree in Computer Science, Physics, Engineering or Math
  • 5+ years of practical work applying ML to solve complex problems for large-scale applications experience
  • 8+ years of processing data with a massively parallel technology (such as Redshift, Teradata, Netezza, Spark or Hadoop based big data solution) experience
  • Knowledge of at least one programming language such as Java, C#, JavaScript, Python, Ruby or Perl
  • Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
  • 7+ years of IT platform implementation in a technical and analytical role experience

Nice to have

  • Master's degree or equivalent in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or experience with AWS Solutions, including EC2, S3, Redshift, EMR (or Hadoop)
  • Experience using managed ML/AI solutions
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with vLLM, SGLang, TensorRT or similar platforms in production environments
  • AWS Professional level certification, or Bachelor's degree in business administration, finance, economics, computer science, data science, engineering, or other related field
  • 4+ years of professional work experience, or experience with AWS services or other cloud offerings
  • Generative AI trends, patterns, anti-patterns

What the JD emphasized

  • 5+ years of practical work applying ML to solve complex problems for large-scale applications experience
  • 8+ years of processing data with a massively parallel technology (such as Redshift, Teradata, Netezza, Spark or Hadoop based big data solution) experience
  • 7+ years of IT platform implementation in a technical and analytical role experience
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with vLLM, SGLang, TensorRT or similar platforms in production environments

Other signals

  • Designing and implementing complex, scalable, and secure AWS solutions tailored to customer needs
  • Providing technical guidance and troubleshooting support throughout project delivery
  • Collaborating with stakeholders to gather requirements and propose effective migration strategies
  • Acting as a trusted advisor to customers on industry trends and emerging technologies
  • Sharing knowledge within the organization through mentoring, training, and creating reusable artifacts
  • Experience using managed ML/AI solutions
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with vLLM, SGLang, TensorRT or similar platforms in production environments
  • Generative AI trends, patterns, anti-patterns