GenAI Data Engineer

Role: Gen AI Data Engineer

Location: Charlotte, NC (Hybrid from Day 1)

Duration: 10+ Months

Job description:

Key Responsibilities:

GenAI Platform & Utility Development

• Design and develop generic, reusable utilities for GenAI data ingestion, context orchestration, and retrieval pipelines.
• Build standardized data movement patterns to move structured and unstructured data between systems, vector stores, and LLMs.
  • Implement MCP-based tooling and services to enable consistent interaction between LLMs, tools, and enterprise data.
  • Develop config-driven and metadata-driven frameworks to support multiple GenAI use cases. RAG Architecture & Implementation
  • Design and implement RAG pipelines including:
• Data ingestion and chunking utilities o Embedding generation and lifecycle management
  • o Retrieval strategies (semantic, hybrid, metadata-based)
  • Build shared components for prompt construction, context enrichment, and grounding.
  • Ensure low-latency, high-quality retrieval across large-scale datasets. API & Integration Development
  • Build and expose robust APIs for GenAI services, utilities, and data movement workflows.
  • Enable integration with upstream and downstream systems through REST and event-driven interfaces.
  • Implement secure authentication, authorization, and rate limiting for AI services. Engineering Excellence & Governance
  • Write production-grade, well-tested, and maintainable code.
  • Implement observability (logging, tracing, metrics) for GenAI utilities.
  • Address key GenAI concerns including:
• Security and access control
• Cost management and optimization
• Prompt/version lifecycle management
  • Document utilities, architectures, and usage patterns for widespread adoption.
  • Collaborate with platform, data, and application teams to define GenAI standards and best practices.

Required Skills & Qualifications:

Core GenAI Skills:

• Hands-on experience with Generative AI / LLM-based systems in production.
• Strong experience with Model Context Protocol (MCP) for tool and data orchestration.
• Proven experience designing and implementing RAG architectures.
• Expertise in prompt design, context management, and retrieval strategies. Data Movement & Platform Skills
• Experience building generic utilities for data ingestion, transformation, and retrieval.
• Strong understanding of unstructured data handling (documents, logs, text, metadata).
• Familiarity with vector databases and embedding workflows.
• Experience designing scalable, reusable platform-level components. API & Software Engineering
• Strong experience designing and implementing RESTful APIs.
• Proficiency in one or more backend languages (Java, Python, or similar).
• Experience with asynchronous processing and event-driven systems.
• Strong understanding of distributed systems and scalability patterns.

Preferred Qualifications:

• Experience building enterprise GenAI platforms or internal tooling.
• Familiarity with agent-based architectures and AI workflows.
• Experience integrating GenAI systems with data platforms and enterprise systems.
• Knowledge of cloud-native architectures (AWS, Azure, or Google Cloud Platform).
• Experience with containerization and CI/CD pipelines.
• Strong focus on responsible AI, governance, and compliance.