Senior AI Architect

Role & responsibilities

The position combines strategic advisory responsibilities with hands-on architecture and implementation leadership. It is suited to professionals who can connect business priorities, enterprise platforms, cloud ecosystems, and AI engineering practices into scalable and reusable solution patterns.

Role Purpose The Senior AI Architect will shape and validate AI-led transformation programs for enterprise clients, with a strong focus on scalable architecture, reusable assets, and outcome-driven solution design. The role is expected to bridge business stakeholders, enterprise architects, platform teams, data and AI engineering teams, and delivery leadership across global engagements.

This role requires practical judgment on how AI should be embedded into enterprise landscapes, including business applications, integration layers, governance controls, and production operations. Strong communication with CXOs and cross-functional stakeholders is central to success in this role.

Key Responsibilities

Define target-state AI and data architecture aligned to business outcomes, enterprise architecture standards, and transformation roadmaps.

• Design end-to-end AI solution architectures covering data pipelines, model development, deployment, monitoring, security, and integration with enterprise applications.
• Build scalable and reusable assets, accelerators, reference architectures, frameworks, and patterns for enterprise AI adoption.
• Evaluate and validate solution options across cloud, AI, and enterprise application platforms such as SAP, Oracle, ServiceNow, and Salesforce.
• Lead architecture reviews, technical due diligence, feasibility studies, and proof-of-concept direction for AI initiatives.

• Collaborate with business leaders, delivery teams, product owners, security teams, and global stakeholders to shape credible and implementable solutions.

• Establish good practices for testing, deployment, observability, lifecycle management, and production support of AI systems.
• Drive architectural governance across responsible AI, compliance, privacy, model risk, and enterprise security requirements.
• Mentor engineering and architecture teams, influence critical technology choices, and support executive-level solution positioning.

Required Experience

• 12+ years of overall experience in enterprise technology, architecture, or digital transformation, with strong recent experience in AI/ML solution architecture and delivery.

• Proven hands-on experience designing and implementing production-grade AI or ML solutions for enterprise use cases.
• Experience across more than one enterprise platform, preferably SAP, Oracle, ServiceNow, Salesforce, or adjacent enterprise ecosystems.
• Experience working in architecture consulting, transformation programs, or complex enterprise modernization engagements.
• Demonstrated ability to work with CXOs, business stakeholders, and multidisciplinary global teams to influence architectural decisions.
• Strong understanding of enterprise integration patterns, API-led architecture, security, scalability, and non-functional design principles.

AI And Technical Skills

AI Architecture

• End-to-end AI/ML architecture, including data ingestion, feature engineering, training pipelines, model serving, monitoring, and lifecycle management.
• Experience with generative AI architecture, including LLM-based applications, retrieval augmented generation, prompt orchestration, guardrails, and evaluation patterns.
• Ability to define reference patterns for recommendation, forecasting, NLP, computer vision, intelligent automation, and enterprise copilots.

Cloud And MLOps

• Strong architecture experience on one or more cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Working knowledge of enterprise MLOps and LLMOps practices such as experiment tracking, model registry, pipeline orchestration, deployment automation, and model monitoring.
• Experience with tools and platforms such as Azure Machine Learning, Amazon SageMaker, Vertex AI, Databricks, MLflow, Kubeflow, DVC, Airflow, or equivalent ecosystems.

AI Engineering Stack

• Hands-on familiarity with Python and common AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, LangChain, LlamaIndex, Hugging Face, or similar tools used in enterprise AI delivery.
• Understanding of vector databases, knowledge re…