Principal Engineer - AI & Machine Learning

Job Summary Responsibilities

• Data Platform Architecture Design and lead modern data platform architecture Build scalable systems for data ingestion, processing, transformation, and storage Enable governed and reliable data access for analytics, ML models, and AI applications
• Data Engineering Build and manage large-scale data pipelines and ETL/ELT systems Implement Data Lake, Data Warehouse, and Lakehouse architectures Ensure scalability, reliability, and performance of data infrastructure
• AI / Machine Learning Engineering Build infrastructure for training, deploying, and monitoring ML models Develop scalable ML pipelines and feature engineering systems Enable AI-powered capabilities within applications
• Generative AI LLM Systems Drive adoption of Generative AI technologies across products Design systems using Large Language Models (LLMs) Build architectures for: LLM integration Retrieval-Augmented Generation (RAG) Vector search systems AI agents and copilots Evaluate and integrate GenAI frameworks and tools
• MLOps AI Infrastructure Build infrastructure for: Model training Model versioning Model deployment Monitoring and observability Experimentation frameworks Establish MLOps best practices for production ML systems
• Data Governance Quality Implement frameworks for: Data lineage Data quality monitoring Access controls Compliance and governance
• AI Adoption Across Products Partner with engineering teams to enable: Predictive analytics Recommendation systems Intelligent automation AI-driven decision systems GenAI-powered product features

Required Qualifications

• 5+ years of hands-on experience building production data and AI/ML systems
• Experience designing and implementing large-scale data platforms and ML infrastructure
• Strong understanding of Generative AI and LLM-based applications in production

Preferred Qualifications

• Experience building AI-powered enterprise platforms
• Experience integrating GenAI features into production systems
• Experience with large-scale data environments
• Familiarity with geospatial or location intelligence data

Key Skills

• Data pipelines
• Data platforms
• Lakehouse architectures
• Streaming and real-time data processing
• ML pipelines
• Feature stores
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Vector databases and embedding systems

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

Disclaimer: The job location mentioned in this description is based on publicly available information or company headquarters. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.