AWS Gen AI Engineer

Role & responsibilities

AWS Gen AI Engineer Senior Associate (Job Description)

About the Role

We are seeking a skilled and experienced AWS Gen AI Engineer to design and develop generative AI applications leveraging Amazon Bedrock, Bedrock AgentCore, and SageMaker.

The ideal candidate will have hands-on experience building GenAI applications using Bedrock foundation models, RAG pipelines with Bedrock Knowledge Bases, and prompt engineering workflows with Bedrock Guardrails.

Key Responsibilities

• Design and develop generative AI applications using Amazon Bedrock, Bedrock AgentCore, and foundation models.
• Build and optimize RAG pipelines using Bedrock Knowledge Bases with agentic retrieval, smart parsing, and multi-modal content support.
• Develop custom ML models using SageMaker (training, fine-tuning, and deployment via JumpStart and HyperPod).
• Implement prompt engineering, optimization, and fine-tuning workflows; leverage Intelligent Prompt Routing for cost optimization.
• Configure and manage Bedrock Guardrails for content safety, PII filtering, and hallucination mitigation with Automated Reasoning checks.
• Design data ingestion pipelines connecting enterprise sources (S3, SharePoint, Confluence, Google Drive, OneDrive, Web Crawler) to Knowledge Bases.
• Conduct model evaluation, benchmarking, and A/B testing across foundation models.
• Integrate GenAI capabilities into production applications using orchestration frameworks (LangChain, LlamaIndex, Strands Agents).
• Provide technical guidance to associates on AI/ML best practices.

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, AI/ML, or related field.
• 5+ years of experience in software engineering or ML engineering.
• 3+ years of hands-on experience with AWS AI/ML services.
• Strong proficiency in building GenAI applications using Amazon Bedrock and foundation models.
• Hands-on experience with SageMaker for model training, fine-tuning, and deployment (JumpStart, HyperPod).
• Experience building RAG pipelines with Bedrock Knowledge Bases and vector stores (OpenSearch Serverless, Pinecone, or similar).
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
• Experience with orchestration frameworks (LangChain, LlamaIndex, CrewAI, or Strands Agents).
• Experience with prompt engineering, optimization, and evaluation techniques.
• Understanding of LLM architectures, tokenization, and inference optimization.
• Experience configuring Bedrock Guardrails for content safety and compliance.
• Knowledge of data engineering for AI/ML (ETL, data preprocessing, feature engineering).
• Strong communication skills and ability to articulate technical decisions.

Nice to Have

• AWS Machine Learning Specialty or AI Practitioner certification.
• Experience with multi-modal AI (vision, audio, text) on Bedrock.
• Familiarity with model monitoring, SageMaker MLOps pipelines, and AI observability.
• Experience with AI tools in development lifecycles (GitHub CoPilot, Cursor, Amazon Q Developer).
• Knowledge of responsible AI frameworks, Automated Reasoning, and bias mitigation.