As a Data & AI Engineer, you’ll build and deploy modern data pipelines and generative AI solutions on AWS — from scalable ETL/ELT workflows and cloud data platforms to production-grade RAG systems and AI agents. One engagement you may be designing and optimizing data pipelines using Glue, Lambda, and Redshift, the next you’re building GenAI applications powered by Bedrock, Claude, and vector databases to enable intelligent search and automation. You’ll work directly with clients to deliver end-to-end data and AI solutions that are secure, scalable, and production-ready.
What You’ll Do:
- Implementing data pipelines using AWS services such as Glue, Lambda, Step Functions, and EMR
- Creating and maintaining data extraction, transformation, and loading processes
- Configuring and optimizing AWS database services including RDS, Aurora, Redshift, and DynamoDB
- Implementing data lakes using S3 and related AWS services
- Designing and building production-ready Generative AI applications using Amazon Bedrock and foundation models such as Anthropic Claude
- Building and optimizing RAG (Retrieval-Augmented Generation) pipelines with vector databases
- Developing AI agents and multi-agent orchestration systems using frameworks like LangChain or LlamaIndex
- Writing and testing SQL queries and stored procedures
- Documenting technical solutions and providing knowledge transfer to customers
- Supporting the implementation of data governance and security controls
- Troubleshooting and resolving issues with data pipelines and AI services
- Participating in code reviews and implementing feedback
- Assisting with proof-of-concept implementations for customer engagements
Required Skills:
- 5+ years of software engineering experience with at least 2+ years focused on AI/ML, data engineering, or cloud-native development
- 2+ years of hands-on AWS experience with production deployments
- 1+ years of direct Generative AI experience (LLMs, embeddings, RAG, agents)
- Proven track record delivering production AI applications from concept to deployment
- Strong understanding of software engineering best practices (version control, testing, code review, documentation)
- Experience working in agile/scrum environments with distributed teams
