This role is for one of the Weekday's clients

Min Experience: 9+ years

Location: Remote (India)
JobType: full-time

We are looking for an experienced Data & AI Architect to lead the design and evolution of modern cloud-based data and AI platforms that enable advanced analytics, machine learning, and generative AI capabilities. This role is responsible for defining enterprise-scale data architecture, building AI-ready data ecosystems, and ensuring secure, scalable, and compliant data solutions.

The ideal candidate combines deep expertise in cloud data architecture, AI platform design, and modern data engineering with the ability to translate business objectives into practical, future-ready technical solutions. You will collaborate closely with engineering, data science, analytics, and business stakeholders to build architectures that power intelligent decision-making across the organization.

Requirements

Key Responsibilities

Enterprise Data & AI Architecture

• Design and evolve enterprise data architecture, including data lakes, data warehouses, data marts, and lakehouse platforms.
• Develop AI-ready data assets that support analytical, predictive, and generative AI use cases.
• Architect cloud-native data platforms on Microsoft Azure for scalable ingestion, transformation, storage, and analytics.
• Establish architectural standards, best practices, and governance for enterprise data platforms.

AI Platform & Solution Design

• Design data architectures that enable machine learning, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI solutions.
• Collaborate with data science teams to develop AI-powered decision automation workflows.
• Design scalable AI infrastructure supporting vector search, embeddings, semantic retrieval, and LLM-based applications.
• Evaluate emerging AI technologies and recommend adoption strategies aligned with business goals.

Cloud Data Engineering

• Architect high-performance Azure-based data solutions using services such as Azure Data Lake, Azure Data Factory, Databricks, Spark, and Azure Machine Learning.
• Design modern data models supporting structured, semi-structured, and unstructured datasets.
• Define scalable ETL/ELT pipelines and real-time data integration architectures.
• Ensure platform performance, scalability, reliability, and cost optimization.

Data Governance & Compliance

• Design secure, explainable, and compliant AI and data architectures aligned with regulatory requirements.
• Implement best practices for data governance, metadata management, master data, lineage, and data quality.
• Support responsible AI practices, model explainability, AI ethics, and model risk management.
• Ensure architectures meet enterprise security, privacy, and compliance standards.

Technical Leadership

• Act as a trusted technical advisor for enterprise data and AI initiatives.
• Guide engineering teams on architectural decisions, design patterns, and implementation best practices.
• Mentor data engineers, architects, and data scientists to promote architectural excellence.
• Review solution designs and ensure consistency with enterprise architecture standards.

Business & Stakeholder Collaboration

• Partner with business stakeholders to understand strategic objectives and translate them into scalable technical solutions.
• Evaluate business requirements and recommend optimal data and AI architectures.
• Communicate architectural trade-offs, risks, and technology decisions to technical and non-technical audiences.
• Support enterprise transformation initiatives through architecture planning and technical governance.

Innovation & Continuous Improvement

• Research emerging cloud, AI, and data technologies to identify innovation opportunities.
• Develop proof-of-concepts and architecture blueprints for new technologies.
• Contribute to enterprise AI strategy and long-term data platform evolution.
• Promote cloud-native, modular, and scalable architectural practices across engineering teams.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
• Extensive experience designing enterprise-scale Data & Analytics platforms.
• Strong experience implementing cloud-native data and AI solutions on Microsoft Azure.
• Deep expertise in data modeling, data warehousing, lakehouse architecture, and enterprise data platforms.
• Strong understanding of APIs, event-driven architecture, master data management, and cloud-native application design.
• Hands-on experience with SQL Server, Azure Data Lake, Azure Data Factory, Databricks, Spark, Python, and CI/CD pipelines.
• Experience designing AI-ready architectures supporting machine learning and generative AI workloads.
• Strong knowledge of MLOps and LLMOps tools such as MLflow, Azure Machine Learning, LangChain, vector databases, and RAG architectures.
• Familiarity with Agentic AI frameworks such as CrewAI, LangGraph, AutoGen, or similar orchestration platforms.
• Excellent analytical, problem-solving, communication, and stakeholder management skills.
• Experience mentoring technical teams and driving architecture governance.

Preferred Qualifications

• Experience within Insurance, Financial Services, or other highly regulated industries.
• Knowledge of regulatory data frameworks and compliance requirements.
• Experience implementing AI governance, explainability, and responsible AI practices.
• Experience working in Agile, Data Mesh, or domain-driven data architectures.
• Microsoft Azure Solutions Architect, Azure AI Engineer, or equivalent cloud certifications.
• AI/ML certifications are an added advantage.

Must-Have Skills

• Data Architecture
• RAG Architecture
• Microsoft Azure
• Azure Data Lake
• Azure Data Factory
• Databricks
• Spark
• Python
• SQL
• Data Modeling
• Data Warehousing
• Lakehouse Architecture
• MLOps
• LLMOps
• LangChain
• Agentic AI

Good-to-Have Skills

• Vector Databases
• Regulatory Compliance
• AI Governance
• Responsible AI
• Azure Machine Learning
• MLflow
• CrewAI
• LangGraph
• Event-Driven Architecture
• Data Mesh
• Master Data Management