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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal AI/ML Engineer, Semantic Data - **Company:** Major League Soccer - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $235,000.0 - $260,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Systems Engineering, Automated Storage and Retrieval Systems, Customer Data Management, Data Architecture, Information Engineering, Data Infrastructure, Information Leak Prevention, Data Systems, Distributed Systems, Graph Database, Monitoring of Systems, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Raw Data, Search Technologies, SQL Databases, Unstructured Data, AI Infrastructure, Large Language Models, Snowflake, Model Validation, Data Layers, Build Management, Information Technology, Operational Systems, Data Management, Databricks - **Published:** June 2, 2026 - **Apply:** https://careers-mlssoccer.icims.com/jobs/2291/principal-ai-ml-engineer%2c-semantic-data/job?in_iframe=1 ## About the Role * Master's degree or higher in computer science, engineering, or related field, or equivalent experience * 8-10+ years of experience in ML engineering, data systems, or applied AI * Strong expertise in Python, SQL, and production software engineering * Deep experience with semantic data modeling, ontologies, and entity resolution * Hands-on experience with embeddings, vector search, and retrieval systems * Experience building and deploying LLM-powered systems including RAG * Experience building production-grade AI systems at scale * Strong understanding of distributed systems and data architecture, * Experience with knowledge graphs and graph databases * Experience designing semantic layers or feature stores * Experience with open-weight LLMs and model adaptation * Familiarity with on-prem or private GPU deployments * Experience with modern data platforms (AWS, Snowflake, Databricks) * Background in marketing analytics, personalization, or customer data platforms ## Description The Principal AI/ML Engineer, Semantic Data will design and build the semantic intelligence layer that enables consistent understanding of fan data, business concepts, and operational workflows across MLS systems. This role combines semantic data systems with applied LLM engineering to build grounded, production-grade AI capabilities. This is a systems engineering role responsible for building and scaling real-world AI infrastructure, including knowledge graphs, retrieval systems, and LLM-powered applications., * Design and implement embedding pipelines across fan data, content, metadata, and behavioral signals * Build metadata and enrichment systems that normalize and structure enterprise data for AI use * Develop knowledge bases and retrieval systems using vector databases and hybrid search architectures * Create context assembly pipelines combining structured data, documents, APIs, and historical outputs * Enable AI systems to operate on unified semantic representations rather than raw data Semantic Layer & Knowledge Graphs * Architect and manage knowledge graphs representing fan, content, and business entity relationships * Define and maintain a semantic layer standardizing metrics, features, and business concepts * Design ontologies, taxonomies, and entity models for fan behavior and identity * Implement graph-based reasoning and enrichment workflows * Ensure semantic consistency across analytics, ML, and operational systems, * Build scalable, production-grade services and APIs for semantic and AI systems * Work with vector and graph databases to support retrieval and reasoning * Integrate structured data, documents, APIs, and model outputs * Partner with data engineering on batch and real-time pipelines * Ensure systems meet performance and reliability requirements, * Design evaluation frameworks for retrieval quality and LLM output correctness * Monitor system performance, relevance, and model behavior * Establish guardrails for explainability, traceability, and data attribution * Ensure safe and reliable generation of structured outputs * Mitigate risks related to bias, data leakage, and inconsistencies, * Collaborate with product, analytics, and engineering teams on AI use cases * Translate business problems into systems combining semantic data and LLM reasoning * Partner with ML teams to improve model performance through better grounding * Mentor engineers and establish best practices ## Related Videos - [Semantic AI: Why Embeddings Might Matter More Than LLMs](https://www.wearedevelopers.com/videos/1460-semantic-ai-why-embeddings-might-matter-more-than-llms) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Beyond SQL Generation: How to Teach Agents What Your Database Actually Means](https://www.wearedevelopers.com/videos/100127-beyond-sql-generation-how-to-teach-agents-what-your-database-actually-means) - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How machine learning can help us tell fact from fiction](https://www.wearedevelopers.com/magazine/509-how-machine-learning-can-help-us-tell-fact-from-fiction)