AI Lead Data Engineer

O projekcie: About Us At Virtusa( former ITMAGINATION), every innovator has the potential to transform and lead in a digital world—but unlocking that potential takes more than technology; it takes a trusted partner who combines engineering excellence, creativity, and an AI-first mindset. Together, we co-create solutions that help businesses grow faster, operate smarter, and make experiences better with technology. The AI Lead Data Engineer acts as the technical lighthouse for our data squads. With 8–10 years of experience, you are responsible for the technical design and delivery of robust AI data platforms. You will bridge the gap between Data Science and Data Engineering, ensuring that our infrastructure supports advanced MLOps and LLM requirements while leading a team of engineers to maintain elite coding and governance standards. Wymagania: - 8–10 years of experience with at least 3 years in a lead role managing technical delivery.   - Expert-level Python, SQL, and PySpark optimization for distributed environments.   - Deep experience with AI platform services such as Amazon SageMaker, Azure ML, or Vertex AI.   - Hands-on experience implementing enterprise observability with Monte Carlo or Datadog.   - Experience with data governance platforms (Collibra/Alation) and implementing Responsible AI guardrails.   - Deep understanding of modern data patterns (Medallion architecture, Data Mesh, Lakehouse).   - Advanced knowledge of security frameworks, including PII masking, data lineage, and HIPAA/GDPR compliance.   - Exceptional ability to mentor junior engineers and communicate complex data strategies to business stakeholders. Codzienne zadania: - Technical Leadership: Lead a squad of data engineers in the design and execution of end-to-end AI data architectures. - AI Observability & Governance: Build frameworks for bias detection, ethical AI considerations, and auditability using platforms like Collibra or Alation. - Infrastructure Design: Lead the transition to Data Lakehouse architectures and implement feature stores for enterprise-wide model reuse. - Delivery Management: Work with stakeholders to manage project milestones, technical risks, and on-time delivery. - Advanced MLOps: Implement enterprise-grade CI/CD for ML workflows using MLflow or Kubeflow. - GenAI Orchestration: Design specialized pipelines for LLM evaluation frameworks and prompt-tuning datasets.