Role Description
Lead the strategy, architecture, implementation, and continuous optimization of enterprise AI platforms that enable Artificial Intelligence (AI), Generative AI, machine learning, and advanced analytics across the organization. This role is responsible for defining AI platform roadmaps, designing scalable and secure AI infrastructure, managing cloud-based AI ecosystems, and ensuring the reliability, governance, and performance of AI services that support business innovation and digital transformation. The successful candidate will collaborate with executive leadership, AI researchers, machine learning engineers, data scientists, data engineers, software developers, cybersecurity teams, enterprise architects, and business stakeholders to deliver enterprise AI capabilities aligned with strategic business objectives. The role also involves implementing MLOps and LLMOps best practices, managing model deployment pipelines, supporting foundation models and Large Language Models (LLMs), establishing AI governance standards, optimizing platform performance, driving cloud modernization initiatives, mentoring technical teams, and continuously improving enterprise AI platform capabilities, security, and operational excellence.
Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems, Information Technology, Data Engineering, Mathematics, Statistics, or a related quantitative field; Master's degree or Ph.D. is highly desirable.
- Proven experience in AI platform engineering, machine learning engineering, MLOps, cloud architecture, data engineering, AI infrastructure, enterprise platform management, or technology consulting, including leadership or strategic advisory responsibilities.
- Strong proficiency in Python, SQL, and programming languages such as Java, Scala, Go, or C++ for AI platform development, automation, and distributed computing.
- Extensive experience designing and managing enterprise AI platforms, including machine learning pipelines, MLOps, LLMOps, feature stores, vector databases, model registries, model serving, inference infrastructure, and AI lifecycle management.
- Hands-on experience with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), including services such as Amazon SageMaker, Azure AI Foundry, Azure Machine Learning, Google Vertex AI, Databricks, Snowflake, Kubernetes, and modern AI infrastructure platforms.
- Strong understanding of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, AI agents, model fine-tuning, responsible AI, AI governance, and enterprise AI security practices.
- Experience implementing DevOps, MLOps, DataOps, CI/CD pipelines, Infrastructure as Code (Terraform or CloudFormation), Docker, Kubernetes, Apache Airflow, MLflow, Kubeflow, monitoring, observability, and platform automation.
- Strong knowledge of enterprise security, identity and access management (IAM), data privacy, regulatory compliance, model governance, disaster recovery, scalability, and high-availability architecture for AI systems.
- Excellent analytical, strategic thinking, leadership, communication, and stakeholder management skills with the ability to define enterprise AI strategies, manage multidisciplinary technical teams, influence executive leadership, and align AI investments with business priorities.
- Experience leading enterprise AI transformation, cloud modernization, AI governance, digital innovation, platform engineering, and large-scale AI deployment initiatives is highly desirable.
- Professional certifications such as Microsoft Certified: Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty (or equivalent current AWS AI certification), Google Professional Machine Learning Engineer, Databricks Certified Machine Learning Professional, Kubernetes (CKA), TOGAF, or equivalent AI, cloud, and enterprise architecture certifications are considered an advantage.
- A visionary, strategic, and innovation-driven technology leader with a passion for building secure, scalable, and high-performance AI platforms that accelerate enterprise AI adoption, empower advanced analytics, enable responsible AI, and deliver sustainable business value through modern artificial intelligence technologies.
