Sr AI Platform Engineer – Retrieval & Knowledge Systems

LPL Financial•Charlotte, NC

11d

About The Position

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.

Requirements

Minimum of 8 years of experience in backend or platform engineering.
Experience building distributed systems, search platforms, or large-scale data services.
Hands-on experience with APIs, microservices, and cloud-native architectures.
Experience with search systems, indexing, or retrieval pipelines.
Experience programming skills in Java, Python, or Go.

Nice To Haves

Experience with cloud platforms and containerized environments (Kubernetes).
Experience with Elasticsearch/OpenSearch, vector databases, or hybrid retrieval architectures.
Familiarity with RAG systems, embeddings, and LLM integration patterns.
Experience building AI platforms, copilots, or agent-based systems.
Experience with real-time data systems (Kafka, streaming pipelines).
Strong understanding of performance optimization in distributed systems.

Responsibilities

AI Retrieval & Search Platform: 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.
Data Pipelines & Indexing Systems: 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.
AI Platform Services & APIs: 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.
LLM & Intelligent Systems Integration: 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.
Performance, Scalability & Reliability: Design for low-latency (<100ms retrieval), high-throughput, and horizontal scalability.
Implement caching, sharding, and distributed query execution strategies.
Build observability pipelines (metrics, logs, tracing) for system performance and usage insights.
Drive resiliency, fault tolerance, and system reliability at scale.
Technical Leadership: Lead design and architecture of large-scale AI platform components.
Mentor engineers on distributed systems, retrieval architectures, and AI engineering practices.
Drive adoption of modern engineering practices (CI/CD, infrastructure-as-code, automated testing).
Partner with AI, data, and product teams to shape next-gen intelligent platform capabilities.