Sr AI Platform Engineer – Retrieval & Systems

Senior Engineer

Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you'll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.

The Senior Engineer will design and build core AI knowledge infrastructure that powers intelligent applications across the enterprise.

This role focuses on distributed systems, retrieval architectures, and AI platform services, enabling applications and agents to discover, retrieve, and reason over large-scale enterprise data. You will work at the intersection of search, vector retrieval, LLMs, and real-time data systems, building high-performance platforms used by AI and application teams.

The ideal candidate is a strong backend/platform engineer with deep experience in building scalable systems and modern AI-powered retrieval architectures.

• Design and build high-scale retrieval systems combining keyword search, semantic search, and vector-based retrieval.
• Develop RAG (Retrieval-Augmented Generation) infrastructure including indexing, retrieval, ranking, and context assembly.
• Build and optimize search indices, vector stores, and hybrid retrieval systems for relevance, latency, and scale.
• Implement advanced ranking, relevance tuning, and personalization pipelines.
• Build streaming and batch pipelines for ingesting and transforming structured and unstructured data.
• Develop enrichment pipelines (chunking, embeddings, metadata extraction, classification).
• Design systems for real-time indexing, incremental updates, and freshness guarantees.
• Optimize data flow, storage, and compute efficiency at scale.
• Build low-latency, highly available APIs that expose retrieval and knowledge services to applications and AI agents.
• Develop reusable SDKs and service abstractions for easy integration into product teams.
• Enable context retrieval, query understanding, and response augmentation for downstream AI systems.
• Establish patterns for multi-tenant, scalable platform services.
• Integrate LLMs with retrieval systems to enable grounded, context-aware experiences.
• Build systems for context construction, prompt augmentation, and response orchestration.
• Implement evaluation frameworks for relevance, grounding quality, and user experience.
• Support use cases like AI assistants, copilots, search experiences, and automation agents.
• Design for low-latency (