Description :

About the Role :

As a Data Architect (Manager level), you will lead enterprise-level solution design for GenAI-enabled, microservices-based, and event-driven architectures across cloud, AI, and data ecosystems.

You will drive architectural governance and technology strategy across multiple platforms, ensuring scalability, performance, and alignment with long-term business and technology goals.

You will also contribute to the development of the Ascend Knowledge Graph Platform, transforming structured and unstructured data into an AI-powered semantic knowledge graph.

Key Responsibilities :

- Design and govern end-to-end technical architecture across enterprise platforms and suites

- Contribute to the design and development of Knowledge Graph platforms for semantic, AI-powered data intelligence

- Develop and integrate data processing pipelines using Python within Azure infrastructure (ADF, Azure Functions, AKS)

- Implement GenAI-powered NLP workflows using LangChain and LangGraph for entity and relationship extraction

- Design and manage data transformation pipelines for Neo4j AuraDB and PostgreSQL

- Build and maintain REST APIs (FastAPI/Flask) for data ingestion, retrieval, and orchestration

- Collaborate with DevOps teams to deploy scalable, event-driven workloads on Azure Kubernetes Service (AKS)

- Implement autoscaling solutions using KEDA and ensure efficient CI/CD practices

- Integrate security and compliance controls using Azure Defender, Key Vault, and Managed Identities

- Support RAG-based search and intelligent retrieval using LangGraph and Neo4j Cypher

- Define and maintain architecture blueprints, standards, and reusable components

- Establish architecture roadmap aligned with long-term platform strategy

- Ensure scalability, performance, and security across microservices and GenAI-enabled systems

- Collaborate with engineering teams to leverage shared services and frameworks

- Evaluate and recommend GenAI technologies and automation opportunities

- Define architecture for agentic workflows, multi-agent orchestration, and tool integrations

- Design RAG patterns including chunking, vector indexing, and embedding strategies

- Maintain consistency across multiple microservices and AI-powered features

- Define CI/CD and infrastructure strategies for resilience and operational efficiency

Key Skills and Qualifications :

- 10 to 14 years of experience in data architecture, cloud, or enterprise systems

- Strong proficiency in Python for backend and data engineering

- Extensive experience with Azure cloud services (ADF, Blob Storage, Service Bus, Functions, AKS)

- Experience with Neo4j or other graph databases and PostgreSQL

- Hands-on experience with LangChain, LangGraph, or similar LLM orchestration frameworks

- Strong understanding of GenAI, RAG architectures, and AI-driven data systems

- Experience with API development (FastAPI/Flask) and microservices architecture

- Knowledge of event-driven architectures and containerized deployments (Kubernetes/AKS)

- Strong understanding of security, governance, and compliance in cloud environments

- Experience defining scalable, reusable, and governed enterprise solutions

Preferred Qualifications :

- Experience with Knowledge Graph platforms and semantic data modeling

- Exposure to KEDA-based autoscaling and advanced DevOps practices