Data Engineer II
Summary
Builds enterprise-grade GenAI and MCP tooling, focusing on RAG, semantic search, and AI-enabled services to power a semantic layer for scalable applications.
Our GenAI Platform team and help build enterprise-grade Generative AI capabilities and MCP (Model Context Protocol) tooling that power our semantic layer. This role is ideal for a Data engineer with a strong technical foundation who is excited about AI technologies, developer platforms, and building intelligent applications at scale.
Reporting to a Senior Engineering Manager or Engineering Director, you will collaborate with engineers, architects, product managers, and data teams to develop scalable, secure, and reliable AI-enabled services that accelerate innovation across the organization.
Design, develop, and maintain services, APIs, and platform components supporting GenAI and semantic-layer capabilities.
Build and enhance MCP tools, adapters, and integrations that enable secure interaction between AI models and enterprise systems.
Implement and optimize retrieval-augmented generation (RAG), semantic search, embeddings, and knowledge retrieval solutions.
Partner with cross-functional teams to integrate AI capabilities into business applications and developer workflows.
Contribute to technical design discussions, architecture reviews, and engineering best practices.
Troubleshoot production issues, improve system reliability, and optimize performance and scalability.
Develop automated testing, monitoring, and observability solutions to ensure operational excellence.
- Stay current on emerging trends in Generative AI, agentic systems, MCP, and semantic technologies.
Bachelor’s degree in computer science, Engineering, Information Technology, or a related field.
2–5 years of software engineering experience developing scalable applications and services.
Proficiency in one or more programming languages such as Python, Java, TypeScript, or Go.
Experience building APIs, microservices, or developer tooling in cloud environments.
Strong understanding of software engineering principles, distributed systems, and system design fundamentals.
Experience with source control, CI/CD pipelines, automated testing, and agile development practices.
Preferred Qualifications
Hands-on experience with Large Language Models (LLMs), Generative AI applications, or AI development frameworks.
Familiarity with vector databases, embeddings, semantic search, and RAG architectures.
Exposure to MCP, agent frameworks, OpenAI tools, LangChain, LlamaIndex, or similar technologies.
Experience with AWS, Azure, or GCP cloud platforms.
Understanding of enterprise security, governance, and responsible AI practices