Position#1
Job Title: Senior AWS AI / Data Engineer
Location: Detroit, MI
Hire Type: Long-term contract
Experience: 7+ years | Detroit, MI (mandatory) — Remote up to 50% travel |
Agentic AI | LLMs | Python | AWS Native | Data Pipelines | Structured + Unstructured Data |
ABOUT THE ROLE
As a Senior AWS AI/Data Engineer at DataFactZ you will architect and deliver enterprise-grade AI and data pipeline solutions for large-scale client engagements. You will lead the design of agentic AI systems, LLM-powered applications, and high-throughput data pipelines on AWS — translating complex business problems into production-ready solutions while mentoring junior engineers.
KEY RESPONSIBILITIES
• Design and build end-to-end data pipelines for ingesting, transforming, and serving structured (SQL, Redshift, Parquet) and unstructured (PDFs, emails, documents, images) data on AWS
• Architect agentic AI systems using LLMs with tool use, memory, and multi-step reasoning via Amazon Bedrock, OpenAI, or Anthropic Claude
• Build multi-agent orchestration workflows using LangChain, LlamaIndex, CrewAI, or AutoGen for enterprise automation
• Design RAG pipelines connecting structured and unstructured data sources to LLMs via vector databases (Pinecone, OpenSearch, pgvector)
• Lead AWS data architecture across S3, Glue, Lambda, EMR, Athena, Step Functions, and Redshift
• Develop prompt engineering strategies and fine-tuning approaches for domain-specific LLM customization
• Mentor junior engineers, lead code reviews, and drive engineering best practices
• Engage client stakeholders to scope AI/data use cases, define success metrics, and deliver on commitments
REQUIRED SKILLS
• Python: Advanced proficiency for data engineering, pipeline orchestration, and AI integrations
• AWS services: Deep hands-on experience with S3, Glue, Lambda, EMR, Athena, Step Functions, Redshift, and Bedrock
• LLMs & Agentic AI: Production experience building LLM-powered agents, tool-calling workflows, and multi-agent systems
• Data pipelines: Batch and real-time ETL/ELT for large-scale structured and unstructured datasets
• RAG & vector search: Building retrieval-augmented generation systems with embedding pipelines and semantic search
• System design: Architecting scalable, secure, cost-efficient cloud-native data and AI systems
• Leadership: Proven ability to lead technical workstreams and communicate designs to senior stakeholders
PREFERRED
• AWS certifications: Solutions Architect, Data Analytics, or Machine Learning Specialty
• Document intelligence: AWS Textract or custom document parsing pipelines
• Multi-modal AI: Experience with vision or document-aware models
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