AWS Gen AI Engineer – Senior Associate

Job Information

• Date Opened 09/02/2026
• Job Type Full time
• Industry IT Services
• Remote Job

Job Description

This is a remote position.

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.