
Data Architect
1 day ago
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.
Required Qualifications
- 12+ years of experience in data architecture, data engineering, and data science.
- Strong programming skills in Python, SQL, and relevant scripting languages.
- Deep understanding of data modeling, ETL/ELT processes, and distributed computing.
- Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch), NLP libraries (e.g., spaCy, Hugging Face), and GenAI tools (e.g., LangChain, LLMs).
- Proven experience with DevOps tools (ADO, Git, Terraform, Kubernetes).
- Expertise in Microsoft Azure preferred
- Familiarity with MLOps practices and model lifecycle management.
Preferred Skills
- Azure certifications (e.g., Azure Solutions Architect, Azure Data Scientist Assoc.).
- Experience with data mesh, real-time analytics, and streaming architectures.
- Knowledge of data privacy regulations (e.g., GDPR, HIPAA).
- Exposure to AI/ML infrastructure and GenAI safety frameworks.
Soft Skills
- Excellent communication and stakeholder engagement skills.
- Ability to lead cross-functional teams and manage competing priorities.
- Strategic thinking with a hands-on approach to execution.
- Passion for innovation, continuous learning, and mentoring. Role & responsibilities
Preferred candidate profile
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