Lead Data AI Engineer

Responsibilities

• Act as Technical Product Owner (TPO) for AI Data Engineering products, capabilities, and platforms.
• Partner with business stakeholders, AI teams, and architects to translate business requirements into scalable data and AI engineering solutions.
• Define and prioritize platform backlogs, technical roadmaps, and delivery plans.
• Drive adoption of reusable data products, AI services, and platform capabilities across multiple business domains.
• Design, develop, and maintain scalable data pipelines supporting AI, Analytics, Machine Learning, and Generative AI use cases.
• Lead implementation of batch, streaming, and real-time data integration capabilities.
• Build trusted and governed data assets supporting enterprise AI use cases.
• Drive engineering standards for data quality, observability, lineage, monitoring, and reliability.
• Ensure data platforms are secure, scalable, resilient, and compliant with enterprise standards.
• Enable AI solution delivery through feature stores, vector databases, model deployment pipelines, and data services.
• Support implementation of Generative AI, LLM, RAG, and Agentic AI architectures through scalable data foundations.
• Collaborate with AI Engineers and Data Scientists to operationalize AI solutions.
• Establish and maintain MLOps and DataOps practices.
• Design and operate cloud-native AI and Data Platforms.
• Define architecture patterns for data ingestion, transformation, storage, governance, and consumption.
• Optimize platform performance, scalability, reliability, and cost efficiency.
• Lead implementation of Infrastructure-as-Code, CI/CD, monitoring, and observability frameworks.
• Lead Agile squads delivering AI Data Engineering and platform capabilities.
• Facilitate sprint planning, backlog refinement, technical reviews, and delivery governance.
• Promote DevOps, DataOps, and Agile engineering best practices.
• Act as the bridge between business stakeholders, AI teams, platform teams, architects, and delivery organizations.
• Ensure compliance with enterprise security, privacy, data governance, and responsible AI requirements.
• Define and monitor OKRs/KPIs related to platform adoption, data quality, delivery velocity, operational performance, and business value realization.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Engineering, Information Systems, Artificial Intelligence, or related disciplines.
• 5–7+ years of experience in Data Engineering, AI Engineering, Platform Engineering, or Cloud Data Platform roles.
• Proven experience designing enterprise-scale data pipelines and cloud-native data platforms.
• Experience acting as Technical Product Owner, Delivery Lead, Lead Engineer, or Squad Lead.
• Strong expertise in ETL/ELT, Data Lakes, Lakehouse architectures, Data Warehousing, Metadata Management, and Data Governance.
• Hands‑on experience with Azure, AWS, or GCP.
• Understanding of Generative AI, LLMs, Vector Databases, RAG, and AI agent architectures.
• Experience implementing MLOps, CI/CD, Infrastructure-as-Code, and DataOps practices.
• Strong SQL and Python skills.
• Experience working in Agile and Scrum environments.

Core Competencies

Demonstrates expertise in designing and implementing scalable data pipelines and cloud-native data platforms, with a strong focus on AI and Data Engineering solutions. Proficient in MLOps, DataOps, and Agile methodologies to drive platform adoption and ensure data quality and compliance.