AI Engineer

Responsibilities:

• Implement end-to-end AI pipelines: data ingestion, embedding generation, retrieval, prompting, response processing, and evaluation.
• Build and maintain RAG pipelines using vector databases (Pinecone, Weaviate, Qdrant, and pgvector) and retrieval frameworks.
• Develop LLM integration layers: API orchestration, streaming, tool-calling, and structured output parsing.
• Write evaluation harnesses to measure LLM output quality, latency, cost, and safety across prompt variations.
• Use AI development tools (Cursor, GitHub Copilot, and Claude Code) to accelerate development and iterate effectively.
• Collaborate with GenAI architects on system design; translate architecture specs into reliable, tested code.
• Build internal and client-facing APIs that expose AI capabilities with proper auth, rate limiting, and observability.
• Hands-on experience with at least one LLM framework: LangChain, LlamaIndex, or direct provider SDKs.
• Understanding of vector search concepts, embedding models, and semantic similarity.
• Comfort working in cloud environments (AWS/GCP/Azure); ability to deploy and monitor service.
• Participate in code reviews and contribute to team standards for AI engineering best practices.

Requirements:

• Strong problem-solving skills with the ability to debug complex AI pipeline failures.
• Willingness to use and learn AI-augmented development workflows as a first-class engineering practice.