Description

You will design and implement end-to-end AI/ML and Generative AI solutions using Python, covering training, evaluation, optimization, and deployment. You will build cloud-native applications on AWS and architect scalable RAG pipelines with vector databases and LLM-powered features.

Responsibilities

• Develop scalable Python microservices using FastAPI or Flask for data pipelines, real-time analytics, and model inference.
• Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM solutions including chatbots, summarization, and semantic search.
• Build and maintain cloud-native applications on AWS using Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS/Aurora, SageMaker, and Bedrock.
• Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and Infrastructure-as-Code using Terraform or CloudFormation.
• Build and support MLOps workflows including model versioning, containerized training/inference, automated retraining, and performance monitoring.

Required Skills

• Strong hands-on experience in Python development for AI/ML and GenAI solutions.
• Proven experience with AWS services for application and ML deployments, specifically Lambda, ECS/Fargate, S3, API Gateway, DynamoDB, RDS/Aurora, SageMaker, and Bedrock.
• Experience building APIs/microservices using FastAPI or Flask.
• Hands-on experience with RAG, embeddings, and vector databases.
• Experience with CI/CD and Infrastructure-as-Code tools such as Terraform or CloudFormation.
• Strong understanding of MLOps practices and production model monitoring.
• 5+ years of professional engineering experience.

Preferred Skills

• Experience with GitHub or GitLab for version control and CI/CD integration.

Key Skills

Education

Any Gradute