AWS Bedrock

Job highlights

• 3+ years in generative AI with AWS Bedrock, prompt engineering, and model fine-tuning
• Design, develop, and deploy generative AI applications using AWS services; collaborate with cross-functional teams; monitor and optimize AI models

JD is below:

• Design, develop, and deploy generative AI based applications using AWS Bedrock.
• Proficiency in prompt engineering and RAG pipeline
• Experience in building Agentic Generative AI applications
• Fine-tune and optimize foundation models from AWS Bedrock for various use cases.
• Integrate generative AI capabilities into enterprise applications and workflows.
• Collaborate with cross-functional teams, including data scientists, ML engineers, and software developers, to implement AI-powered solutions.
• Utilize AWS services (S3, Lambda, SageMaker, etc.) to build scalable AI solutions.
• Develop APIs and interfaces to enable seamless interaction with AI models.
• Monitor model performance, conduct A/B testing, and enhance AI-driven products.
• Ensure compliance with AI ethics, governance, and security best practices.
• Stay up-to-date with advancements in generative AI and AWS cloud technologies.

Required Skills & Qualifications:

• Bachelor's or Masters degree in Computer Science, AI, Machine Learning, or a related field.
• 3+ years of experience in AI/ML development, with a focus on generative AI.
• Hands-on experience with AWS Bedrock and foundation models.
• Proficiency in Python and ML frameworks.
• Experience with AWS services such as SageMaker, Lambda, API Gateway, DynamoDB, and S3.
• Experience with prompt engineering, model fine-tuning, and inference optimization.
• Familiarity with MLOps practices and CI/CD pipelines for AI deployment.
• Ability to work with large-scale datasets and optimize AI models for performance.
• Excellent problem-solving skills and ability to work in an agile environment.

Preferred Qualifications:

• AWS Certified Machine Learning – Specialty or equivalent certification.
• Experience in LLMOps and model lifecycle management.
• Knowledge of multi-modal AI models (text, image, video generation).
• Hands-on experience with other cloud AI platforms (Google Vertex AI, Azure OpenAI).
• Strong understanding of ethical AI principles and bias mitigation techniques.