Assistant Vice President

The Data & AI Architect will be responsible for designing and implementing scalable, secure and modern data and AI architectures that enable enterprise analytics, machine learning, Generative AI and AI-led business solutions.

The role will work closely with business, product, engineering, data science, cloud, cybersecurity and enterprise architecture teams to translate business requirements into robust data and AI solution architectures, ensuring that solutions are scalable, reusable, governed and production-ready.

Key Responsibilities

1. Data Architecture

* Design end-to-end enterprise data architectures supporting analytical, operational and AI workloads.
* Develop data architecture for data lakes, lakehouses, data warehouses and data platforms.
* Define data models, data domains, data products and data flows.
* Design solutions for structured, semi-structured and unstructured data.
* Architect batch, real-time and event-driven data pipelines.
* Define data ingestion, integration, transformation and consumption patterns.
* Design APIs and data-sharing mechanisms for secure data consumption.
* Establish data lineage, metadata and data quality requirements.
* Support modernisation of legacy data architectures and migration to cloud platforms.

2. AI Architecture

* Design end-to-end architectures for AI/ML, Generative AI and intelligent decisioning solutions.
* Translate AI use cases into scalable production architectures.
* Design the complete AI lifecycle:

Data → Feature Engineering → Model → Deployment → Monitoring → Feedback → Continuous Improvement

* Define architecture for machine-learning platforms, model serving and inference.
* Design architectures for predictive analytics, recommendation engines, decision engines and intelligent automation.
* Work with data scientists and ML engineers to operationalise models.

3. Generative AI Architecture

* Design enterprise architectures using Large Language Models (LLMs).
* Develop architecture patterns for:
* RAG
* Vector databases
* Embeddings
* Prompt engineering
* Model orchestration
* AI gateways
* Knowledge bases
* Knowledge graphs
* Agentic AI
* Tool/API integration
* Design multi-model architectures where appropriate.
* Address scalability, latency, cost, security and reliability of GenAI solutions.
* Establish mechanisms for LLM evaluation, observability and performance monitoring.

4. Data & AI Platform Architecture

* Design cloud-based Data & AI platforms using AWS, Azure or GCP.
* Define architecture for scalable compute, storage and processing.
* Evaluate and recommend appropriate technologies and platforms.
* Design reusable architecture components and reference patterns.
* Ensure interoperability across data, AI, application and integration platforms.
* Balance architectural quality with cost, performance and time-to-market.

5. MLOps / LLMOps

* Design architectures supporting automated model development and deployment.
* Define MLOps/LLMOps patterns for:
* Model lifecycle management
* Model registry
* CI/CD
* Model deployment
* Model monitoring
* Model drift
* Model evaluation
* Version management
* Retraining
* Design production-grade AI systems with appropriate observability and operational controls.

6. Data Governance, Security & Responsible AI

* Incorporate security, privacy and governance by design.
* Define appropriate controls for sensitive and confidential data.
* Design role-based access, encryption and secure data-sharing mechanisms.
* Support data quality, lineage and auditability.
* Incorporate responsible AI principles including:
* Explainability
* Transparency
* Human oversight
* Model risk controls
* Bias monitoring
* AI safety
* Work with cybersecurity, risk and compliance teams to ensure architectural compliance.

7. Solution Architecture & Delivery

* Convert business requirements into detailed Data & AI solution architectures.
* Produce architecture diagrams, data flows, integration patterns and technical specifications.
* Evaluate solution alternatives and recommend the most appropriate architecture.
* Provide technical guidance to engineering and development teams.
* Review solution designs and ensure alignment with enterprise architecture standards.
* Participate in architecture reviews and technology evaluations.
* Support PoCs and prototypes and help transition successful solutions into production.
* Troubleshoot complex architectural and integration challenges.

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Key Technical Skills

Data Architecture

* Data modelling
* Data lakes / Lakehouse
* Data warehouse
* Data mesh / data fabric concepts
* Data products
* MDM
* Metadata management
* Data lineage
* Data quality
* ETL / ELT
* Data integration
* Streaming / real-time data

AI / ML

* Machine Learning
* Predictive analytics
* NLP
* Deep Learning
* Recommendation systems
* Decision engines
* Model serving
* MLOps

Generative AI

* LLMs
* RAG
* Vector databases
* Embeddings
* Prompt engineering
* LLM orchestration
* AI agents / Agentic AI
* Knowledge graphs
* Model evaluation
* LLMOps

Cloud & Technology

* AWS / Azure / GCP
* Cloud data platforms
* Databricks / Snowflake or equivalent platforms
* Kafka / event streaming
* API architecture
* Microservices
* Containers / Kubernetes
* Python
* SQL