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: Principal 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 Principal 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 transform fragmented business information into reusable knowledge products with clear ownership, lineage, provenance, permissions, and controls.

You will provide technical leadership for enterprise knowledge platforms and retrieval capabilities that make business policies, procedures, controls, and operational knowledge AI-ready and safely consumable at scale.

Roles & Responsibilities:

Knowledge Platform Architecture: Own the solution architecture and technical design of core knowledge platform capabilities, including ingestion, enrichment, knowledge graph, retrieval, and lifecycle management.

Ontology & Semantic Architecture: Lead the design and evolution of the enterprise ontology, semantic model, taxonomy, and metadata standards to create consistent knowledge across business domains.

Enterprise Knowledge Products: Architect and deliver high-quality, AI-ready knowledge products that transform unstructured business knowledge into trusted, governed, and reusable assets.

Knowledge Governance & Guardrails: Design and build governance and guardrail controls into the platform, including provenance, lineage, policy-aware access, classification, permissions, and auditability.

RAG, GraphRAG & Agentic AI: Lead integration of the knowledge platform with LLM gateways, agent frameworks, and evaluation services to support safe RAG, GraphRAG, and agentic AI use cases.

Engineering Standards: Establish engineering best practices, reusable architecture patterns, quality standards, and conduct design and code reviews for critical components.

Technical Leadership & Mentoring: Mentor Staff and Senior Engineers and work with product owners, SMEs, and platform teams to translate complex business requirements into scalable technical solutions.

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.

RAG, GraphRAG & Retrieval:

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

Experience building enterprise search, recommendation, discovery, and knowledge marketplace capabilities is desirable.

Knowledge Graph & Semantic Technologies:

Hands-on experience with RDF, OWL, SKOS, SPARQL, ontology engineering, taxonomy management, Neo4j, Amazon Neptune, and graph modelling concepts.

Enterprise Knowledge Platform Architecture:

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

Ability to define reusable technical patterns and architecture across ingestion, enrichment, retrieval, knowledge graphs, governance, and lifecycle management.

Knowledge Governance & Responsible AI:

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

Experience designing AI guardrails, retrieval controls, policy-aware access mechanisms, and governance capabilities to ensure trusted and authorised knowledge consumption.

GenAI & Agentic AI:

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 proficiency in Python as the primary programming language, along with TypeScript/Node.js and SQL for platform engineering, APIs, data pipelines, and AI integrations.

Risk & Compliance:

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

Understanding of process intelligence, process mining, business capability modelling, and decision modelling is desirable.

Leadership & Stakeholder Collaboration:

Strong technical leadership with the ability to establish engineering practices and mentor Staff and Senior Engineers.

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

Strong problem-solving, analytical, communication, and documentation skills.

Ability to work across engineering, CAIO, CDO, business SMEs, product owners, and platform teams.

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

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.