Minneapolis, Minnesota, United States
• Engineered GenAI-powered applications using Python, LangChain, RAG pipelines, and Agentic AI to deliver real-time, context-aware assistance within Target’s Store Companion platform, supporting 400,000+ users.
• Built scalable backend microservices and vector retrieval systems leveraging PostgreSQL, Kafka, and embedding stores, enabling fast semantic search, long-term memory, tool calling, and LLM-driven reasoning workflows.
• Developed and optimized API/function services in Kotlin to orchestrate LLM calls, manage embeddings, and support autonomous, agent-like task execution using planning, tool routing, and contextual grounding.
• Contributed to frontend development using React & TypeScript to surface LLM-generated insights and interactive workflows, ensuring seamless full-stack integration between AI systems and UI components.
• Developed machine learning pipelines for prompt chaining, model evaluation, feature logging, embedding generation, and vector indexing, collaborating with data and platform teams to ensure scalable, high-quality model integration into production systems.
• Implemented production-grade observability—metrics, logging, dashboards—to ensure system reliability, safety, and performance at scale.