Description

You will design and implement end-to-end AI/ML and Generative AI solutions using Python. You will build cloud-native applications on AWS and operationalize RAG pipelines with LLM-powered automation.

Responsibilities

• Design and implement end-to-end AI/ML and Generative AI solutions, including model training, evaluation, optimization, and deployment.
• Build and maintain cloud-native applications on AWS using Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS/Aurora, SageMaker, and Bedrock.
• Develop high-performance Python microservices (FastAPI/Flask) to enable scalable data pipelines, model inference, and real-time analytics.
• Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM-powered automation for chatbots, summarization, semantic search, and anomaly detection.
• Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and infrastructure-as-code (Terraform/CloudFormation) for reliable, automated deployments.

Required Skills

• 5+ years of experience with Python and AI/ML development.
• Strong proficiency in Generative AI (GenAI) concepts and implementation.
• Hands-on experience with AWS services: Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS, SageMaker, and Bedrock.
• Experience building microservices using FastAPI or Flask.
• Proficiency in CI/CD tools such as GitHub, GitLab, or AWS CodePipeline.
• Experience with Infrastructure-as-Code using Terraform or CloudFormation.
• Bachelor's degree in Computer Science, Engineering, or a related field.

Preferred Skills

• Experience building robust MLOps workflows, including model versioning, containerized training/inference, automated retraining, and monitoring.
• Familiarity with vector databases and embedding techniques for RAG architectures.

Key Skills

Education

Bachelor's degree