Staff Platform Engineer
CommBank
Staff Platform Engineer
CommBank
Full-spectrum financial services and banking
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 AI/ML and knowledge platform 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.
AI/ML Platform Development: Design and develop scalable, production-grade AI/ML platforms and capabilities supporting enterprise AI, knowledge management, retrieval, and GenAI use cases.
Ontology, Taxonomy & Knowledge Graphs: Develop and maintain enterprise ontology, semantic models, metadata, taxonomy, and knowledge graphs that provide shared context across business domains.
Knowledge Platform Engineering: Engineer knowledge ingestion, enrichment, retrieval, APIs, integrations, 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:
Python Engineering:
Minimum 10+ years of strong hands-on experience in Python development, with experience building enterprise-grade applications, platforms, APIs, data pipelines, and/or AI/ML solutions.
Strong software engineering fundamentals and experience developing scalable, reliable, secure, and reusable platform components.
AI/ML Platform Development:
Strong hands-on experience designing and developing enterprise AI/ML platforms and production-grade AI capabilities.
Experience with scalable platform architecture, APIs, integration patterns, data/AI pipelines, observability, security, and cloud-native engineering.
Knowledge Management & Ontology Development:
Strong understanding and hands-on experience in Knowledge Management and ontology development, including knowledge graphs, 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 experience 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:
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 proficiency in Python with 10+ years of hands-on development experience as a key requirement, together with TypeScript/Node.js and SQL exposure 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 AI/ML platform, 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.
If you're already part of the Commonwealth Bank Group (including Bankwest, x15ventures), you'll need to apply through Sidekick to submit a valid application. We’re keen to support you with the next step in your career.
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Commonwealth Bank of Australia (CommBank) provides a full range of financial services in Australia, including retail, premium, business and institutional banking, funds management, superannuation, insurance, investments and sharebroking. Customers use accounts, loans, investments and insurance through multiple channels with digital tools and advisory services to manage money, borrow, invest and plan for the future. It differentiates itself by its size, scale and breadth, offering a broad suite of services under one umbrella to serve the Australian market. Its goal is to help all Australians build and manage their finances and to engage with customers and communities.
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