Organization:

At CommBank, we never lose sight of the role we play in other people’s financial wellbeing. Our focus is to help people and businesses move forward to progress. To make the right financial decisions and achieve their dreams, targets, and aspirations. Regardless of where you work within our organisation, your initiative, talent, ideas, and energy all contribute to the impact that we can make with our work. Together we can achieve great things.

Job Title: Staff Engineer – Knowledge Management

Location: Bangalore

Business & Team:

The Chief AI Office (CAIO) drives the bank’s AI strategy, platforms, governance, and capabilities to accelerate the safe and scalable adoption of AI across the enterprise.

The Knowledge Management domain within CAIO creates the cognitive foundation of the bank by transforming business knowledge into trusted, governed, and reusable knowledge products.

As a Staff Engineer – Knowledge Management, you will work closely with Knowledge Governance & Framework Leads, Knowledge Platform & Engineering Teams, Business SMEs, knowledge owners, product teams, and DHP platform and DevSecOps teams.

Impact & Contribution:

You will directly support CAIO's objective of creating the bank's enterprise knowledge capability and trusted knowledge products that accelerate AI-powered transformation across the Group.

You will enable colleagues, copilots, and AI agents to access trusted, governed, and reusable enterprise knowledge, improving decision-making consistency and productivity.

You will help reduce duplication of knowledge, manual research effort, and rework by transforming fragmented information into reusable knowledge products with clear ownership, lineage, and controls.

You will build enterprise knowledge platform and retrieval capabilities that make business policies, procedures, controls, and operational knowledge AI-ready and safely consumable at scale.

You will strengthen governance, explainability, and risk management through metadata, provenance, permissions, evaluation, and auditability built into the knowledge lifecycle.

Roles & Responsibilities:

Enterprise Knowledge Products: Design and build enterprise-scale knowledge products that transform unstructured information into trusted, reusable, and AI-ready assets.

Ontology, Taxonomy & Knowledge Graphs: Develop and maintain the enterprise ontology, semantic model, metadata, taxonomy, and knowledge graph that provide shared context across business domains.

Knowledge Platform Engineering: Engineer knowledge ingestion, enrichment, retrieval, and lifecycle management capabilities for governed knowledge products.

Knowledge Governance: Build and enforce knowledge governance controls covering ownership, lineage, provenance, classification, permissions, auditability, and lifecycle policies.

Policy-as-Code & Decisioning: Implement policy-as-code and deterministic decisioning capabilities to deliver consistent, explainable, and compliant AI outcomes.

AI Guardrails & Retrieval Controls: Develop AI guardrails, retrieval controls, and policy-aware access mechanisms to ensure only authorised and trusted knowledge is exposed to LLMs and agents.

AI & Agent Integration: Integrate the knowledge platform with LLM gateways, agent frameworks, evaluation frameworks, and AI platform services to accelerate safe adoption of agentic AI solutions.

Quality, Evaluation & Observability: Establish quality, observability, evaluation, and feedback loops to continuously improve knowledge accuracy, trustworthiness, retrieval effectiveness, and business outcomes.

Stakeholder Collaboration: Work across engineering, CAIO, CDO, product teams, business SMEs, and knowledge owners to translate complex business knowledge, policies, processes, and controls into reusable enterprise knowledge products.

Essential Skills:

Knowledge Management & Semantic Engineering:

Deep expertise in knowledge graphs, ontology engineering, semantic modelling, taxonomy, and metadata management for enterprise-scale knowledge ecosystems.

Strong experience in unstructured data processing, document understanding, information extraction, entity resolution, and knowledge enrichment pipelines.

Strong stakeholder engagement and product-thinking skills with the ability to translate business knowledge, policies, processes, and controls into reusable enterprise knowledge products.

RAG, GraphRAG & Retrieval:

Hands-on expertise in Retrieval-Augmented Generation (RAG), GraphRAG, vector databases, search technologies, and retrieval optimisation for AI-powered knowledge systems.

Experience building enterprise search, retrieval, discovery, recommendation, or similar knowledge capabilities is desirable.

Knowledge Graph & Semantic Technologies:

Hands-on knowledge of semantic and graph technologies including RDF, OWL, SKOS, SPARQL, ontology engineering, taxonomy management, Neo4j, Amazon Neptune, and graph modelling concepts.

Enterprise Knowledge Platform Engineering:

Proficiency in designing enterprise knowledge platforms with scalability, reliability, observability, security, and cloud-native architecture principles.

Knowledge of ingestion pipelines, enrichment, retrieval, lifecycle management, APIs, integration patterns, and enterprise platform engineering.

Knowledge Governance & Responsible AI:

Strong understanding of knowledge governance, data governance, lineage, provenance, access control, compliance, permissions, classification, and auditability frameworks.

Experience implementing AI guardrails, retrieval controls, policy-aware access, evaluation mechanisms, and governance controls for safe consumption of enterprise knowledge.

GenAI & Agentic AI Integration:

Experience integrating LLM gateways, agent frameworks, AI platforms, guardrails, evaluation frameworks, and model orchestration services.

Familiarity with agentic AI architectures, multi-agent orchestration, MCP patterns, and tool-based reasoning systems is desirable.

Policy-as-Code & Decisioning:

Ability to implement policy-as-code, executable knowledge, rules engines, and deterministic decisioning patterns to support explainable and compliant AI outcomes.

Experience with BPMN, DMN, policy engines, and executable knowledge is desirable.

Programming Languages & Tools:

Strong programming experience with Python as the primary language, together with TypeScript/Node.js and SQL for platform engineering, APIs, data pipelines, and AI integrations.

Experience with cloud-based AI, knowledge management, semantic technologies, or knowledge graph platforms is desirable.

Risk, Compliance & Enterprise Knowledge:

Knowledge of risk, compliance, regulatory, and control frameworks within highly governed financial services environments is advantageous.

Understanding of process intelligence, process mining, business capability modelling, and decision modelling to capture and operationalise organisational knowledge is desirable.

Collaboration & Engineering Capability:

Strong problem-solving and analytical skills.

Ability to work effectively across engineering, CAIO, CDO, and business stakeholders.

Clear communication and documentation skills with the ability to explain complex knowledge architecture, governance, and AI concepts to technical and non-technical audiences.

Strong ownership mindset with a focus on automation, continuous improvement, scalability, and reusable engineering practices.

Education Qualification:

Bachelor’s degree or Master’s degree in Engineering, Computer Science, Applied Mathematics, Statistics, or a related discipline.

Relevant cloud or AI certifications, including AWS, AI/ML, or Generative AI certifications, are advantageous.