Knowledge Engineering Senior Manager

Job description

Who we are: Accenture Song Service helps leading brands turn customer interactions into an engine for growth, moving service beyond a cost center to something that builds trust, loyalty, and share of wallet. Our Service practice works end to end: we shape the vision and strategy for the service function and help clients implement and run it, innovate the products and services customers rely on, equip client customer service and customer experience teams with the right technology and talent, and turn customer data into insights that strengthen client’s relationship with their customers. Service AI & Data puts AI at the heart of this work. We help clients reinvent how they serve their customers and support their people: conversational AI and AI agents that resolve customer needs across voice, chat, and messaging; and copilots that help service agents and field teams do their best work. All of this frontline work is powered by a trusted knowledge base that provides the right information, processes, and systems for every interaction. We also build the data foundations, data lakes, reporting, and analytics agents that turn every interaction into insight. The strategies we shape and the experiences we design are delivered into production at enterprise scale. Who you are: A Knowledge Engineering leader who is ready to turn our clients' customer service knowledge into a strategic asset. You lead the complete knowledge engineering scope on our most strategic and complex service programs, building a single, trusted source of truth for service knowledge that grounds AI agents, supports service agents, and gives customers consistent answers across every channel. You set strategy and architecture end to end, from ontologies and knowledge graphs to the governance that keeps knowledge accurate as products, policies, and customer needs change. You are a confident client partner and team leader with deep experience managing senior stakeholder relationships. You will need to inspire and challenge a design, unblock a team, and tell a client the hard truth about what it takes to get AI into production. You develop knowledge engineers into leaders, define what staying at the forefront means for the discipline, and form clear points of view that help clients navigate how AI is reshaping customer service. The work: In this role you will have accountability across three dimensions: Client Delivery: Oversee knowledge engineering across multiple programs, providing technical direction and quality oversight while maintaining accountability for delivery health, team performance, and measurable outcomes such as answer accuracy, first-contact resolution, handle time, and knowledge reuse. Stay connected to the work: inspire and challenge your teams' designs, unblock delivery when it stalls, and give clients a candid view of what it takes to get AI into production. Own the strategy and architecture for a single source of truth for service knowledge, consolidating knowledge bases, documents, product and policy data, and case history into a governed knowledge graph and semantic layer. Set the standards for how service ontologies and taxonomies are designed, evaluated, and maintained across programs. Make knowledge the trusted foundation for AI, defining how it grounds virtual agents, agent-assist copilots, and analytics agents, in partnership with AI Solution Engineering and Enterprise AI Architecture leaders. Establish knowledge governance and operating models with clients, including ownership, review cycles, content lifecycle, access rules, and feedback loops that turn knowledge gaps found in service interactions into new content. Shape knowledge around how customers and service agents actually look for answers, translating customer experience journeys and conversation designs into knowledge structures that support them. Business Development: Lead Accenture sales and pre-sales efforts, shaping service knowledge and data transformation programs that turn one project into a lasting partnership. Make and defend the business case for the semantic layer and knowledge graph solution at the executive level, connecting knowledge quality to service outcomes and cost-to-serve. Develop trusted-advisor relationships with senior client stakeholders, including service, customer experience, and knowledge management leaders. Drive thought leadership on knowledge as the foundation for AI in customer service, positioning Song as a credible and compelling voice in the industry. People & Practice Development: Lead, coach, and grow teams of knowledge engineers, helping them grow their technical depth, leadership skills, and client acumen so they can step up as workstream and program owners. Build inclusive, high-performing teams where practitioners are supported, challenged, and able to grow into stronger leaders over time. Lead the creation of reusable assets that advance the discipline beyond any single engagement, such as service ontologies, knowledge models, curation pipelines, and accelerators. Bring AI-augmented delivery into your teams, applying AI tooling across story grooming, solutioning, testing and triage, and development to raise speed and quality. Here's what you'll need: Minimum 6 years of experience with knowledge graph and semantic technologies (RDF, SPARQL, LPG, SHACL), as part of 10+ years in data, AI, search, or information architecture. Minimum 6 years of experience in schema design, ontology management, and knowledge graph curation. Minimum 4 years designing and developing knowledge graph solutions and graph-based ML models (functional and technical). Minimum 3 years of experience with end-to-end data pipeline implementations for AI applications (especially LLMs), with hands-on design and configuration. Minimum 6 years of experience with relational databases, object stores, graph databases (Stardog, Neo4j, Amazon Neptune), and vector databases. Minimum of 5 years experience leading knowledge, data, or AI transformation in customer service, contact center, or customer experience, with a working understanding of service metrics such as containment, first-contact resolution, average handle time, CSAT, and cost-to-serve, and how knowledge quality moves them. Minimum 6 years of managerial experience leading people, with the ability to explain the value of the semantic layer and knowledge graphs to senior business and technology stakeholders, and a proven track record in sales and/or pre-sales, delivering data transformation programs, and developing teams. Bachelor's degree or equivalent (12 years working experience). Bonus points if you have: Experience with service knowledge management platforms such as Salesforce Knowledge, ServiceNow Knowledge Management, eGain, or Zendesk. Experience establishing Knowledge-Centered Service (KCS) or similar knowledge management practices at enterprise scale. Experience with GraphRAG or other knowledge-grounded retrieval for AI agents. Practical experience with NLP and/or search techniques, prompt engineering, and LLMs for enterprise-scale applications. 5+ years of hands-on experience with cloud platforms (AWS, Azure, Google Cloud). 5+ years of experience in Python, with frameworks such as TensorFlow or PyTorch and tools for building ETL pipelines, such as Apache NiFi or Airflow. Broad experience in diverse ML techniques and agentic systems. External client-facing consulting experience. Ph.D. in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field. You contribute to external thought leadership, such as publishing POVs, speaking at events, or representing the practice in the broader AI community. Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below. We