Sr AI Platform Engineer – Retrieval & Knowledge Systems

LPL Financial; Fort Mill (SC); On-site; USD 115,154 - 191,889; Full time

Benefits offered by this job

401(k) matching

Health benefits

Employee stock options

Paid time off

Volunteer time off

Job summary

LPL Financial is seeking a Senior Engineer to design and build the core AI knowledge infrastructure, enabling intelligent applications. This role involves developing retrieval systems, APIs, and optimizing data flow while mentoring other engineers.

Candidates should have a minimum of 8 years of experience in backend engineering, and expertise in distributed systems or search platforms is essential. Competitive pay range and total rewards package are offered.

Qualifications

• 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.

Responsibilities

• Design and build high-scale retrieval systems combining keyword search and semantic search.
• Develop Retrieval-Augmented Generation infrastructure including indexing and ranking.
• Build low-latency, highly available APIs for knowledge services.

Skills

Backend engineering

Distributed systems

APIs

Cloud-native architectures

Java

Python

Go

Tools

Kubernetes

Elasticsearch/OpenSearch

Kafka

Job description

Job Overview

The Senior Engineer will design and build core AI knowledge infrastructure that powers intelligent applications across the enterprise, focusing on distributed systems, retrieval architectures, and AI platform services. The role enables applications and agents to discover, retrieve, and reason over large‑scale enterprise data at the intersection of search, vector retrieval, LLMs, and real‑time data systems.

Responsibilities

• 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 such as AI assistants, copilots, search experiences, and automation agents.

• 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.

• 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‑generation intelligent platform capabilities.

Qualifications

• 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.
• Proficient in Java, Python, or Go.

Preferences

• 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.

Pay Range

$115,154.00 – $191,889.00 (actual base salary varies by skill, experience, education, peer comparison, and location).

LPL Total Rewards package includes 401(k) matching, health benefits, employee stock options, paid time off, volunteer time off, and more.

Company

LPL Financial Holdings Inc. – a leading wealth‑management firm.

Equal Opportunity Employer

Principal positions only. EOE.