19 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Design and review scalable data architectures including warehouses, lakehouses, data platforms, graph stores, and vector stores for analytics and AI workloads.
- Define data quality, lineage, governance, data contract, and data product standards for cross-product adoption.
- Define reference architectures for RAG, agentic AI, and LLM-integrated systems, including safety filtering, bias assessment, human-in-the-loop controls, and audit evidence.
- Advise on agentic AI interoperability protocols and emerging identity and authorization standards.
- Align data and AI architecture decisions with company-wide standards through partnership with product architects.
- Represent data engineering and applied AI perspectives in architecture review boards and standards discussions.
- Communicate architectural trade-offs to executive and non-technical stakeholders and influence direction across product lines.
- Mentor other architects informally on data engineering and applied AI practices.
Requirements
- 10+ years of experience in software architecture, data engineering, or AI/ML systems, or equivalent demonstrated depth.
- Bachelor’s degree in Computer Science or equivalent professional experience.
- Experience operating as a software architect across multiple teams or products.
- Demonstrated ability to design production-scale data architectures using warehouse, lakehouse, data platform, or layered medallion patterns.
- Hands-on experience architecting applied AI systems such as production RAG pipelines and LLM-integrated services.
- Experience building cloud-native applications on AWS, Azure, or GCP, including containerized applications deployed on Kubernetes.
- Master’s degree in Computer Science or a related field is preferred.
- Experience in cross-product or cross-organizational architecture at a similarly sized enterprise software company is preferred.
- Breadth across data warehousing, lakehouses, data platforms, graphs, vector technologies, metadata management, and streaming architectures is preferred.
- Familiarity with ODCS, ODPS, Unity Catalog, Google Cloud Dataplex Universal Catalog, Apache Atlas, pgvector, Milvus, Kafka, Flink, Delta Live Tables, MCP, and A2A is preferred.
- Experience with graph databases, knowledge graphs, vector databases, embedding pipelines, and production RAG systems is preferred.
- Familiarity with agentic AI safety patterns such as prompt injection defenses, guardrails, and PII handling is preferred.
Tech Stack
Categories
AI ApplicationsData Engineering
About OpenText
OpenText builds enterprise information management software and cloud services spanning content services, customer communications, B2B/EDI integration, eDiscovery, and cybersecurity. It sells licenses, subscriptions, and managed services to large enterprises and governments, with offerings available on-premises and in the cloud. Founded in 1991 and headquartered in Waterloo, Ontario, OpenText (NASDAQ/TSX: OTEX) has expanded through major acquisitions, including Micro Focus (2023) and Documentum (2017).