Principal Engineer - AI and Machine Learning

As a Principal Engineer - AI and Machine Learning at our company, you will play a crucial role in leading the architecture and technical strategy for our data platforms, AI/ML systems, and GenAI infrastructure. Your focus will be on technical excellence, innovation, and cross-functional collaboration to build scalable AI-powered enterprise platforms and enable intelligent product capabilities across the organization. Key Responsibilities: - Data Platform Architecture: - Design and drive modern enterprise data platform architecture. - Build scalable systems for data ingestion, processing, transformation, and storage. - Enable reliable and governed data access for analytics, ML models, and AI applications. - Data Engineering: - Develop and manage large-scale data pipelines and ETL/ELT systems. - Implement modern data architectures such as: Data Lake, Data Warehouse, Lakehouse Architecture. - 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 product teams to integrate AI-powered capabilities into applications. - Generative AI and LLM Systems: - Drive adoption of Generative AI technologies across products. - Design systems using Large Language Models (LLMs) for intelligent automation and data-driven applications. - Build architectures for: LLM Integration, Retrieval-Augmented Generation (RAG), Vector Search Systems, AI Agents and Copilots. - Evaluate and integrate modern GenAI frameworks and tooling. - MLOps and AI Infrastructure: - Build and maintain infrastructure for: Model Training, Model Versioning, Model Deployment, Monitoring and Observability, Experimentation Frameworks. - Establish MLOps best practices for reliable production ML systems. - Data Governance and Quality: - Implement frameworks for: Data Lineage, Data Quality Monitoring, Access Controls, Compliance and Governance, and AI Adoption Across Products. - Partner with product engineering teams to enable: Predictive Analytics, Recommendation Systems, Intelligent Automation, AI-driven Decision Systems, GenAI-powered Product Features. Qualifications Required: - Strong experience in distributed data processing and data pipelines, Data Lake / Lakehouse architectures, Streaming and real-time data processing, Large-scale analytics platforms, Machine Learning Systems. - Hands-on experience with ML pipelines and feature stores, Model training and deployment, ML model monitoring and lifecycle management, and Generative AI. - Strong understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Vector databases and embedding systems, AI agents and copilots, Prompt engineering and LLM orchestration frameworks. - 5+ years of hands-on experience in building and architecting production-grade Data and AI/ML systems. - Proven experience in designing and implementing large-scale data platforms and ML infrastructure. - Strong expertise in Generative AI and LLM-based production applications. In addition, if you have experience building AI-powered enterprise platforms, integrating GenAI capabilities into production systems, exposure to large-scale data environments, or familiarity with geospatial or location intelligence data, it would be considered as preferred qualifications for this role. As a Principal Engineer - AI and Machine Learning at our company, you will play a crucial role in leading the architecture and technical strategy for our data platforms, AI/ML systems, and GenAI infrastructure. Your focus will be on technical excellence, innovation, and cross-functional collaboration to build scalable AI-powered enterprise platforms and enable intelligent product capabilities across the organization. Key Responsibilities: - Data Platform Architecture: - Design and drive modern enterprise data platform architecture. - Build scalable systems for data ingestion, processing, transformation, and storage. - Enable reliable and governed data access for analytics, ML models, and AI applications. - Data Engineering: - Develop and manage large-scale data pipelines and ETL/ELT systems. - Implement modern data architectures such as: Data Lake, Data Warehouse, Lakehouse Architecture. - 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 product teams to integrate AI-powered capabilities into applications. - Generative AI and LLM Systems: - Drive adoption of Generative AI technologies across products. - Design systems using Large Language Models (LLMs) for intelligent automation and data-driven applications. - Build architectures