PURPOSE & OVERALL RELEVANCE FOR THE ORGANIZATION:
This role Senior Manager - AI Architect is to lead the architecture design, technical
governance, and solution blueprinting of enterprise AI and Generative AI solutions. This role will act as the horizontal AI architecture authority across multiple projects and delivery pods, translating business opportunities into scalable, secure, compliant, and production-ready AI solutions.
AI / GENAI SOLUTION ARCHITECTURE
- Design end-to-end AI and GenAI solution architectures, including predictive ML,
recommendation engines, RAG-based applications, AI copilots, intelligent automation, and
multimodal AI scenarios.
- Define reusable architecture patterns for LLM integration, prompt orchestration, vector search,
embedding pipelines, agentic workflows, and model serving.
- Translate business requirements into solution blueprints, architecture diagrams, integration
patterns, and implementation guidance.
- Evaluate and recommend appropriate model strategies, including commercial LLMs, open-
source models, local deployment options, and hybrid approaches.
ENTERPRISE ARCHITECTURE & GOVERNANCE
- Act as the design authority for AI solutions, ensuring architectural consistency, reusability,
scalability, maintainability, and alignment with enterprise technology standards.
- Establish AI reference architectures, design principles, architecture decision records, and
technical review gates.
- Partner with data/platform architects to ensure AI solutions are properly integrated with
enterprise lakehouse, data pipelines, APIs, and business applications.
- Drive technical governance for AI solution delivery, including design reviews, production
readiness reviews, and vendor technical assessments.
DATA PLATFORM, MLOPS AND LLMOPS INTEGRATION
- Define architecture for AI workloads across data ingestion, feature preparation, embedding
generation, retrieval, inference, evaluation, monitoring, and feedback loops.
- Guide MLOps and LLMOps practices, including model lifecycle management, prompt/version
control, automated evaluation, CI/CD, observability, and model performance monitoring.
- Ensure AI solutions leverage trusted, governed, and high-quality data sources with appropriate
lineage, access control, and monitoring.
- Define non-functional requirements for AI systems, including latency, availability, reliability,
throughput, cost efficiency, and operational supportability.
SECURITY, COMPLIANCE AND RESPONSIBLE AI
- Design AI solutions in compliance with applicable data privacy, cybersecurity, data residency,
and internal governance requirements.
- Define controls for PII protection, role-based access control, data masking, audit logging,
human-in-the-loop review, and sensitive data handling.
- Establish GenAI guardrails, including grounding strategy, hallucination mitigation, prompt
injection protection, content safety controls, and usage monitoring.
- Work closely with legal, security, compliance, and data governance stakeholders to ensure AI
risks are identified and mitigated before production release.
STAKEHOLDER ENGAGEMENT & TECHNICAL LEADERSHIP
- Partner with business teams to identify high-value AI use cases and shape feasible, measurable,
and scalable solutions.
- Provide technical leadership to AI engineers, data engineers, QA, product teams, and external
vendors during solution delivery.
- Communicate complex AI concepts, trade-offs, risks, and recommendations clearly to both
technical and non-technical stakeholders.
- Mentor project teams and help uplift enterprise AI architecture and engineering maturity.
- Global and local IT
- Respective business function
REQUISITE EDUCATION AND EXPERIENCE / MINIMUM QUALIFICATIONS:
- 10+ years of professional experience in technology, software engineering, data engineering,AI/ML, or solution architecture.
- 5+ years of experience in solution architecture, enterprise architecture, or technical leadership roles.
- 3+ years of hands-on or architecture experience with AI/ML solutions in production
environments.
- Practical experience with GenAI/LLM solutions such as RAG, AI chatbot/copilot, knowledge assistant, semantic search, or AI workflow automation.
- Experience working in complex enterprise environments with cross-functional teams, global stakeholders, and external technology vendors.
- Strong understanding of AI/ML fundamentals, model lifecycle, data science workflows, model serving, evaluation, and monitoring.
- Solid understanding and hands-on experiences of GenAI architecture patterns, including LLM APIs, embeddings, vector databases, retrieval orchestration, prompt engineering, and agent-based workflows.
- Experience with Python, REST APIs, microservices, containerization, and cloud-native solution design.
- Experience with data platforms such as lakehouse architecture, data pipelines, real-time/batch integration, and governed data products.
- Familiarity with MLOps/LLMOps tools and practices such as MLflow, model registry, CI/CD pipelines, automated evaluation, monitoring, and observability.
- Experience with cloud platforms such as Alibaba Cloud, AWS; Alibaba AI stack experience is a plus.
- Experience with vector databases or semantic search technologies such as Azure AI Search,FAISS, Milvus, Pinecone, Elasticsearch/OpenSearch, or equivalent technologies.
