Generative AI Expert - AWS
•Today
| Verified Pay | $50 per hour |
|---|---|
| Hours | Part-time |
| Location | Sacramento, California |
About this job
Job Description
Job Description
Job Overview:
We are seeking a highly skilled and experienced Generative AI Expert with deep expertise in AWS to lead the development and deployment of cutting-edge generative AI solutions. The ideal candidate will have a strong background in machine learning, deep learning (particularly transformer-based models), and cloud-native development on AWS.
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.
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