Registration of Interest for #AIDA Talent Acceleration Day (23 Sept 26) - Snr AI Knowledge Engineer

An empowering career at Singtel begins with a Hello. Our purpose, to Empower Every Generation, connects people to the possibilities they need to excel. Every "hello" at Singtel opens doors to new initiatives, growth, and BIG possibilities that takes your career to new heights. So, when you say hello to us, you are really empowered to say…“Hello BIG Possibilities”.

Build AI. Unlock BIG Possibilities with Singtel.

Singtel’s Artificial Intelligence & Data Analytics team, AIDA, is growing its team of AI, data and cybersecurity and technology professionals.

We are inviting experienced AI Knowledge Engineer to register their interest in current and upcoming opportunities.

How You will Make An Impact:

• Responsible for building, integrating, and operating AI knowledge system components, including knowledge graphs, structured knowledge formats, memory stores, vector databases, and retrieval pipelines that support GenAI and agentic AI use cases.
• Develop and maintain data preparation, indexing, embedding, and retrieval workflows using tools such as Databricks, Python, SQL, vector databases, and graph databases, applying sound engineering practices for reliability and maintainability.
• Contribute to the delivery of reliable, secure, and reusable knowledge assets for enterprise search, retrieval-augmented generation, agent memory, and AI assistant use cases.
• Implement and enhance knowledge base, retrieval-augmented generation, and memory-store solution components under approved architecture patterns to support scalable Generative AI, Machine Learning, Analytics and Agentic AI use cases.
• Implementing real-time context stores that give Agents a way to commit into and access shared customer contexts
• Build and maintain knowledge ingestion, transformation, entity extraction, embedding, indexing, and retrieval workflows using platforms such as Databricks, Python, SQL, graph databases, and vector databases, ensuring reliability, traceability, and maintainability.
• Perform data transformation, cleansing, entity normalization, relationship extraction, metadata enrichment, and schema mapping using Python, PySpark, SQL, or graph query languages based on business and technical requirements.
• Monitor and troubleshoot data workflows to ensure data quality and pipeline reliability
• Support code quality, deployment readiness, documentation, and production support activities for knowledge-system components, working with senior engineers, architects, and delivery partners where required.
• Contribute to integration of structured and unstructured content from files, APIs, databases, enterprise systems, and streaming sources, working with source-system owners and consuming teams to implement approved ingestion and knowledge-modelling patterns.
• Help maintain metadata and pipeline documentation for transparency and traceability
• Own production readiness for assigned data and AI platform components, including observability, incident triage, root-cause analysis, release coordination, and continuous improvement of operational runbooks.
• Participate in integrating pipelines with tools such as Microsoft Fabric, Databricks, Delta Lake, and other platform components
• Build and maintain knowledge graph components, including entity models, relationship mappings, ontology-aligned schemas, graph loading jobs, and graph query interfaces to support AI retrieval and reasoning use cases.
• Implement and operate knowledge storage and retrieval components, including document stores, vector databases, embedding pipelines, semantic indexing, memory stores, lifecycle management, and access-controlled retrieval.
• Contribute to work process automation efforts using version control and CI/CD workflows, and
• Implement structured knowledge representations and interchange formats, including JSON-LD, RDF-style triples, ontology-aligned schemas, and Google Open Knowledge Format where applicable, to support reusable and machine-readable enterprise knowledge assets.
• Apply data governance, security, access control, privacy, and operational risk policies during implementation, ensuring knowledge-system components handle enterprise content safely and meet approved compliance requirements.

Skills for Success:

• Bachelor’s degree in Computer Science, Engineering, or a related field
• 5 years of experience in data engineering, AI engineering, knowledge engineering, or cloud-scale analytics solution delivery, with hands-on experience building production or near-production data, search, RAG, or knowledge-system components.
• Proven ability to independently build, test, and support production or near-production knowledge-system components, including data preparation, entity extraction, embedding workflows, indexing, retrieval, monitoring, and operational support.
• Hands-on experience with Python and SQL for data transformation and validation
• Familiarity with Apache Spark (especially PySpark) and large-scale data processing concepts
• Hands-on experience with at least one vector database, graph database, search platform, or knowledge-store technology, and familiarity with embeddings, semantic retrieval, metadata filtering, and access-controlled retrieval patterns.
• Self-starter with strong problem-solving skills and a keen attention to detail
• Able to work independently while collaborating effectively with senior engineers, architects, product owners, source-system teams, and business stakeholders to translate requirements into secure and maintainable knowledge-system components.
• Strong documentation and communication skills
• Working knowledge of AI knowledge-system concepts, including knowledge graphs, ontologies or schemas, Google Open Knowledge Format, vector databases, memory stores, embedding pipelines, retrieval evaluation, access control, CI/CD, and production support practices.

Are you ready to say hello to BIG Possibilities?

Join Singtel to shape what's next and accelerate your career through meaningful work, continuous learning, and real impact.