Evaluate and recommend the right tools, frameworks, cloud services, and integration patterns for AI initiatives.y
Partner with product, engineering, data, and business teams to translate business needs into architecture designs and implementation roadmaps.
Provide architectural guidance to solution architects, engineers, and project teams throughout the delivery lifecycle.
Present architecture options, trade-offs, and target-state roadmaps to senior leadership and non-technical stakeholders.p
Guide best practices for AI/ML engineering, MLOps, model lifecycle management, and platform scalability.
Drive the adoption of reusable architectural components, design patterns, and engineering standards across AI programs.
Stay current with emerging AI technologies and assess their applicability to enterprise use cases and platform evolution.
Responsible to ensure compliance with applicable external and internal regulations, procedures, and guidelines.
Living Hitachi Energy’s core values of safety and integrity, which means taking responsibility for your own actions while caring for your colleagues and the business.
Your Background
Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field.
8+ years of experience in technology roles, with significant experience in AI/ML solution architecture and enterprise platform design.
Proven track record of architecting and delivering complex AI solutions in enterprise environments.
Strong expertise in Python, AI/ML frameworks, data architecture, APIs, and system integration patterns.
Hands-on experience with cloud platforms (Azure, AWS, or GCP), MLOps, containerization, and scalable deployment architectures.
Strong understanding of security, privacy, governance, and responsible AI principles in solution design.
Experience working in a global, cross-functional environment with distributed delivery teams.
Exposure to enterprise AI applications in sectors such as energy, manufacturing, utilities, healthcare, finance, or retail.
Certifications in cloud architecture, AI/ML, data engineering, or enterprise architecture frameworks.
Experience defining reusable reference architectures, governance models, and technology standards for AI adoption at scale.
Excellent communication and stakeholder management skills, with the ability to influence technical and business decisions.
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