Generative AI Expert AWS

Job Overview:

Required Skills and Qualifications:

• Must have proven experience in building and deploying

  generative AI models

  (such as LLMs, GANs, diffusion models).
• Must have deep expertise in

  AWS AI/ML stack

  : SageMaker, Bedrock, EC2, Lambda, S3, CloudWatch, IAM, etc.
• Must have strong programming skills in

  Python

  (TensorFlow, PyTorch, Transformers, LangChain, etc.).
• Must have

  experience with MLOps tools

  (e.g., SageMaker Pipelines, MLflow, Kubeflow).
• Must be familiar with

  Retrieval-Augmented Generation (RAG) using AWS-native services

  and frameworks.
• Must have strong understanding of

  AWS architecture

  , networking, and security best practices.
• Must have experience in creating a RAG system for document search using

  Amazon Kendra and LangChain

  .
• Must have experience in building a

  secure, scalable chatbot using a fine-tuned LLM on AWS

  .
• MUST HAVE

  AWS Certified Machine Learning Specialty

  ,

  Solutions Architect Professional Certification(s).

• Excellent communication and documentation skills.
• Experience with Amazon Bedrock, AWS Inferentia, and AWS Trainium would be a plus
• Prior experience with multi-modal models (e.g., image + text) and use cases like synthetic media generation would be a plus.
• Must have background in NLP, CV, or RL research would be a plus.
• Must have experience with CI/CD for ML (e.g., CodePipeline, CodeBuild).
• Prior experience in developing generative image or video pipelines for marketing using AWS infrastructure.
• Prior experience in integrating third-party LLM APIs via Bedrock and deploying with custom prompt engineering pipelines.

Key Responsibilities:

• Design, develop, and deploy generative AI

  models (e.g., LLMs, diffusion models) using

  AWS services

  .
• Architect scalable, secure, and cost-effective

  ML infrastructure on AWS

  (e.g.,

  Sagemaker

  , Lambda, ECS, EKS).

• Fine-tune foundation models

  (e.g., LLaMA, Falcon, Claude, Mistral) using Amazon SageMaker or custom pipelines.
• Collaborate with data scientists, ML engineers, and DevOps to operationalize AI models into production.
• Implement

  real-time inference pipelines

  using AWS services such as SageMaker Endpoints, Lambda, and API Gateway.
• Evaluate and integrate third-party APIs (e.g., Bedrock, OpenAI, Anthropic) with custom workflows.
• Optimize model training and inference performance using GPU-based instances (e.g., EC2 P4d, G5) and distributed training.
• Ensure compliance with security, privacy, and governance policies when deploying models in AWS environments.
• Stay up to date with the latest in generative AI research, tools, and AWS innovations.