Data and AI Lead Architect

Role Overview: You will be playing a crucial role as a Data and AI Lead Architect in designing and implementing scalable, secure, and intelligent data and AI solutions across the enterprise. Your focus will be on shaping the data and AI strategy with an emphasis on cloud-native architectures, advanced analytics, and emerging AI technologies. Your deep technical expertise, strategic thinking, and passion for innovation will be instrumental in driving impactful business outcomes. Key Responsibilities: - Architect and lead the development of enterprise-scale data and AI platforms, ensuring alignment with business goals and technology strategy. - Design and implement robust data architectures, AI/ML pipelines, and scalable solutions using Google Cloud Platform (GCP) with Big Query expertise. - Drive the adoption of LLM-based solutions and explore opportunities to integrate Agentic AI frameworks into enterprise workflows. - Collaborate with cross-functional teams including engineering, product, and business stakeholders to translate requirements into high-impact technical solutions. - Define and enforce architectural standards, best practices, and governance models for data and AI systems. - Ensure compliance with data security, privacy, and regulatory requirements across all initiatives. - Provide technical leadership and mentorship to engineering and data science teams. - Stay current with advancements in AI, cloud technologies, and data platforms to inform architectural decisions and innovation roadmaps. - Support program delivery by identifying architectural risks, dependencies, and trade-offs. Qualifications Required: - Advanced degree in Computer Science or related field, with 7+ years leading enterprise AI, data, and analytics programs. - Proven success in architecting and delivering large-scale AI/ML solutions across cloud platforms such as GCP, AWS, Azure. - Deep expertise in LLMs, transformer models, and end-to-end AI system design. - Strong programming and data engineering skills in Python, R, SQL, Spark, and Hadoop. - Proficiency in modern data platforms like BigQuery, Snowflake, and AI/ML frameworks such as TensorFlow and PyTorch. - Skills in cloud-native architecture, containerization (Docker), and orchestration (Kubernetes). - Experience in MLOps practices including CI/CD, model deployment, monitoring, and rollback. - Strong leadership abilities in managing cross-functional teams and large technology portfolios. - Excellent communication skills with the ability to influence stakeholders across technical and business domains. - Commitment to ethical AI, data privacy, and regulatory compliance. (Note: Additional details about the company were not provided in the job description.),