AWS AI/ML

Responsibilities

• Design, develop, and deploy scalable AI/ML solutions using AWS services such as Amazon Bedrock, SageMaker, Amazon Q, Amazon Lex, Amazon Connect, and Lambda.
• Implement and optimize large language model (LLM) applications using Amazon Bedrock, including prompt engineering, fine-tuning, and orchestration for specific business use cases.
• Build and maintain end-to-end machine learning pipelines using SageMaker for model training, tuning, deployment, and monitoring.
• Integrate conversational AI and virtual assistants using Amazon Lex and Amazon Connect, with seamless user experiences and real-time inference.
• Leverage AWS Lambda for event-driven execution of model inference, data preprocessing, and microservices.
• Design and maintain scalable and secure data pipelines and AI workflows, ensuring efficient data flow to and from Redshift and other AWS data stores.
• Implement data ingestion, transformation, and model inference for structured and unstructured data using Python and AWS SDKs.
• Collaborate with data engineers and scientists to support development and deployment of ML models on AWS.
• Monitor AI/ML applications in production, ensuring optimal performance, low latency, and cost efficiency across all AI/ML services.
• Ensure implementation of AWS security best practices, including IAM policies, data encryption, and compliance with industry standards.
• Drive the integration of Amazon Q for enterprise AI-based assistance and automation across internal processes and systems.
• Participate in architecture reviews and recommend best-fit AWS AI/ML services for evolving business needs.
• Stay up to date with the latest advancements in AWS AI services, LLMs, and industry trends to inform technology strategy and innovation.
• Prepare documentation for ML pipelines, model performance reports, and system architecture.

Qualifications we seek in you:

Minimum Qualifications

• Proven hands-on experience with Amazon Bedrock, SageMaker, Lex, Connect, Lambda, and Redshift.
• Strong knowledge and application experience with Large Language Models (LLMs) and prompt engineering techniques.
• Experience building production-grade AI applications using AWS AI or other generative AI services.
• Solid programming experience in Python for ML development, data processing, and automation.
• Proficiency in designing and deploying conversational AI/chatbot solutions using Lex and Connect.
• Experience with Redshift for data warehousing and analytics integration with ML solutions.
• Good understanding of AWS architecture, scalability, availability, and security best practices.
• Familiarity with AWS development, deployment, and monitoring tools (CloudWatch, CodePipeline, etc.).
• Strong understanding of MLOps practices including model versioning, CI/CD pipelines, and model monitoring.
• Strong communication and interpersonal skills to collaborate with cross-functional teams and stakeholders.
• Ability to troubleshoot performance bottlenecks and optimize cloud resources for cost-effectiveness

Preferred Qualifications:

• AWS Certification in Machine Learning, Solutions Architect, or AI Services.
• Experience with other AI tools (e.g., Anthropic Claude, OpenAI APIs, or Hugging Face).
• Knowledge of streaming architectures and services like Kafka or Kinesis.