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.

This is a remote position.