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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Engineer, AI Retrieval & Knowledge Platforms - **Company:** LPL Financial - **Location:** Fort Mill, SC, United States (Remote available) - **Experience:** Expert - **Salary:** $115,154 - $191,889 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Computing Platforms, Automated Storage and Retrieval Systems, Automation of Tests, Cloud Computing, Computer Programming, Continuous Integration, Shard (Database Architecture), DevOps, Distributed Data Store, Distributed Systems, Elasticsearch, Fault Tolerance, Graph Database, Information Retrieval, Python (Programming Language), Knowledge-Based Systems, Performance Tuning, Microsoft Copilot, Search Technologies, Data Streaming, Unstructured Data, Retrieval-Augmented Generation, Large Language Models, Reliability of Systems, Indexer, Core Api, Backend, Agentic-AI, Event Driven Architecture, Build Management, Containerization, AI Platforms, Kubernetes, Real Time Data, Apache Kafka, Build Tools, Search Engines, Stream Processing, Data Pipelines, OpenSearch, Microservices - **Published:** October 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=521a518cde87d391 ## About the Role We're looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work., * Minimum of 8 years building large-scale backend, platform, or distributed systems. * Hands-on experience building search platforms, retrieval systems, indexing pipelines, knowledge systems, or recommendation platforms. * Strong knowledge of APIs, microservices, Kubernetes, containers, CI/CD, and distributed data processing.Experience programming skills in Java, Python, or Go. * Experience building enterprise platforms in highly regulated environments such as: + Wealth Management + Brokerage / Broker-Dealer, * Experience building AI platforms, copilots, or agent-based systems. * 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 with real-time data systems (Kafka, streaming pipelines). * Strong understanding of performance optimization in distributed systems. * Financial Services, Wealth Management, Brokerage, or Advisor Technology experience. ## Description 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., 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., + Capital Markets + Insurance + FinTech + Healthcare Core Competencies: * RAG (Retrieval Augmented Generation) * Semantic Search * Vector Databases * Embeddings * LLM Integration Patterns * Knowledge Graphs * Real-Time Data Platforms * Kafka / Streaming Systems * Observability and Monitoring * Platform APIs * Event-Driven Architecture * Multi-Tenant Platform Design