Data Architect

Key Responsibilities
Architectural Leadership Skills- Design and implement scalable, secure, and high-performance data architectures to support AI/ML workloads, analytics, and real-time data processing.- Architect data platforms that enable experimentation, model training, and deployment for classical ML, deep learning, NLP, and GenAI use cases.- Lead the development of data lakes, feature stores, and analytical data marts optimized for both operational and research use cases.- Define and enforce architectural standards, data governance policies, and best practices across data science, engineering, and analytics teams.- Collaborate with cross-functional teams to align data architecture with business strategy and analytical goals.Technical Expertise & Experience- Translate analytical and AI/ML requirements into scalable data infrastructure and pipelines.- Develop and support end-to-end ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring.- Hands-on experience with Classical ML algorithms (e.g., regression, decision trees, ensemble methods), Deep Learning architectures (CNNs, RNNs, Transformers), and NLP models (BERT, GPT, T5).- Architect and integrate Generative AI solutions using LLMs, prompt engineering, and retrieval-augmented generation (RAG) techniques.- Implement DevOps practices including CI/CD pipelines, infrastructure as code, automated testing, and monitoring.- Ensure high standards of code quality, modularity, and reusability across data and ML pipelines.- Integrate modern data technologies such as Spark, Kafka, Delta Lake, and NoSQL databases.- Collaborate with data scientists to design feature stores, model registries, and scalable ML infrastructure.- Build and optimize data solutions on Microsoft Azure, including Azure Copilot Studio, Azure AI Foundry, Azure Data Factory, Synapse Analytics, Azure Databricks, Blob Storage, and Azure Machine Learning.

Role & responsibilities

Preferred candidate profile