Knowledge Operations Manager

Job Description: Knowledge Operations Manager

Department: Customer Experience / AI Operations
Experience: 3–6 years
Location: Mumbai, Kandivali
Employment Type: Full-time

About the Role

We are looking for a Knowledge Operations Manager to lead a team of human operators responsible for researching, preparing, validating, and maintaining high-quality answers to customer queries.

The role will establish a structured knowledge operations process in which customer questions are converted into accurate, consistent, reusable answers, response templates, FAQs, and standard operating procedures (SOPs). These knowledge assets will enable customer-facing human agents to resolve queries efficiently and consistently.

The individual will manage the operator team, define quality standards, validate responses, identify gaps in available information, and ensure that approved answers remain up to date. The role will also contribute to building an AI-ready knowledge base that can support AI copilots, conversational agents, and automated customer support in the future.

Key Responsibilities

1. Human Operator Management

• Recruit, onboard, train, and manage a team of knowledge research and response preparation operators.
• Allocate incoming queries and research tasks based on complexity, priority, and operator expertise.
• Establish clear workflows, turnaround times, productivity targets, and quality standards.
• Monitor operator performance and provide regular feedback, coaching, and training.
• Ensure operators follow defined research, validation, documentation, and escalation procedures.

2. Answer Preparation and Knowledge Management

• Oversee the research and preparation of accurate, relevant, and customer-friendly answers to frequently asked and recurring customer queries.
• Convert unstructured information from websites, product documentation, internal teams, policies, and other approved sources into structured answers.
• Create and maintain FAQs, response templates, troubleshooting guides, decision trees, and SOPs.
• Ensure that answers are clear, concise, consistent, and aligned with approved company policies and brand communication guidelines.
• Categorise and tag answers by topic, customer intent, product, query type, and other relevant attributes.
• Establish a centralised, searchable repository of approved answers for use by customer-facing agents.

3. Quality Assurance and Validation

• Define quality assurance criteria covering factual accuracy, completeness, relevance, clarity, tone, and source reliability.
• Review and approve answers before they are published or made available to customer-facing agents.
• Implement a review process for ambiguous, sensitive, complex, or high-risk queries.
• Maintain source references and ensure answers are supported by reliable, approved information.
• Identify recurring errors, inconsistencies, and knowledge gaps and implement corrective actions.
• Establish periodic reviews to retire outdated answers and update content when policies, products, or processes change.

4. Query Analysis and Continuous Improvement

• Analyse customer queries to identify recurring questions, emerging topics, and unmet information needs.
• Identify queries that cannot be answered confidently using existing knowledge.
• Collaborate with relevant internal teams to obtain clarifications and resolve information gaps.
• Recommend new FAQs, response templates, and knowledge articles based on query volume and business impact.
• Improve the answer preparation process through standardisation, reusable templates, workflow optimisation, and appropriate AI-assisted tools.

5. AI Readiness and Knowledge Enablement

• Structure approved answers so they can be consumed by AI-powered customer support tools and agent-assist platforms.
• Ensure knowledge assets are organised, searchable, traceable to their sources, and maintained through defined ownership and review processes.
• Work with Product and Technology teams to make approved knowledge available through relevant systems and integrations.
• Support the evaluation of AI-generated answers against human-validated reference answers.
• Analyse incorrect or incomplete AI responses and coordinate updates to the underlying knowledge, instructions, or response logic.
• Contribute to the gradual adoption of AI-assisted research, response drafting, quality checks, and knowledge maintenance.

Required Skills and Qualifications

• Bachelor's degree in Business Administration, Communications, English, Information Management, or a related discipline.
• 3–6 years of experience in knowledge management, customer support operations, content operations, research operations, quality assurance, or a similar role.
• At least 1–2 years of experience managing a team or coordinating the work of multiple operators, researchers, or content specialists.
• Excellent written communication, research, editing, and information synthesis skills.
• Strong attention to detail and the ability to distinguish verified information from assumptions.
• Experience creating FAQs, response templates, knowledge articles, process documentation, or SOPs.
• Ability to define operational workflows, monitor productivity, and enforce quality standards.
• Strong analytical and problem-solving skills, including the ability to identify recurring patterns in customer queries.
• Proficiency with spreadsheets, documentation tools, knowledge management systems, and collaboration platforms.

Preferred Qualifications

• Experience with customer support platforms, helpdesks, CRM systems, or knowledge management tools.
• Familiarity with conversational AI, chatbots, AI copilots, or generative AI applications.
• Understanding of prompt design, knowledge retrieval, and AI response evaluation.
• Experience working with structured content, taxonomies, metadata, and searchable knowledge repositories.
• Exposure to quality assurance frameworks, service-level agreements (SLAs), and operational performance dashboards.

Key Performance Indicators (KPIs)

• Answer Accuracy: Percentage of answers meeting defined factual accuracy and validation standards.
• Quality Acceptance Rate: Percentage of submitted answers approved without major rework.
• Turnaround Time: Average time taken to research, prepare, review, and publish an answer.
• Knowledge Coverage: Percentage of priority customer query categories covered by approved answers.
• Operator Productivity: Number of validated answers produced per operator within the defined period.
• Knowledge Freshness: Percentage of knowledge assets reviewed and updated within the prescribed review cycle.
• Reusability: Percentage of approved answers reused by customer-facing agents rather than recreated from scratch.
• Knowledge Gap Resolution: Percentage of identified knowledge gaps resolved within agreed timelines.

What Success Looks Like

The successful candidate will build a reliable knowledge operations function in which a trained team of human operators consistently produces accurate, validated, and reusable answers. Customer-facing agents will have access to a trusted source of information, reducing research effort, response preparation time, inconsistencies, and avoidable escalations.

Over time, the knowledge repository will also provide a validated foundation for AI-assisted support and automated customer interactions.