AI Engineer - Lead
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Key skills for this role
Role Overview
The TWIN AI Engineer role is focused on knowledge engineering for Twin agents.
The Twin Knowledge Engineering Specialist builds and maintains pipelines that enable reliable, explainable, and grounded answers.
The position supports the Production STA – AIOPS department in Mumbai.
Key Skills for This Role
Full Job Posting
Role Overview
The TWIN AI Engineer role is focused on knowledge engineering for Twin agents.
The Twin Knowledge Engineering Specialist builds and maintains pipelines that enable reliable, explainable, and grounded answers.
The position supports the Production STA – AIOPS department in Mumbai.
Knowledge Engineering
- Engineer ingestion workflows for documents, procedures, FAQs, incident knowledge, changes, infrastructure knowledge, and operational standards.
- Extract and normalize structured and unstructured content, enrich metadata, and manage document versioning and lifecycle metadata.
- Prepare content for RAG indexing and graph-based representation.
- Define chunking strategies, semantic tagging, indexing strategies, and metadata models.
- Support hybrid search, semantic search, graph-assisted retrieval, and source citation capabilities.
Knowledge Graphs
- Model IT production entities and relationships for graph-based knowledge used by Twin.
- Populate graph structures from documentation, CMDBs, inventories, and operational sources.
- Support entity resolution, deduplication, relationship validation, graph quality checks, and GraphRAG use cases.
- Work with Twin Core and graph specialists on ontology, schema, and graph evolution.
Quality and Source Controls
- Implement checks for missing metadata, obsolete documents, duplicate or conflicting content, missing owners, and missing validation dates.
- Monitor source freshness and knowledge coverage by domain or franchise.
- Maintain source certification status and implement exposure rules by agent, franchise, domain, and confidentiality level.
- Ensure traceability, access control, data protection, source usage monitoring, and auditability.
Retrieval Improvement
- Analyze failed retrieval cases and identify missing, poorly structured, or incorrectly indexed knowledge.
- Tune search and retrieval parameters and improve chunking and metadata strategies.
- Build evaluation datasets and compare expected sources with retrieved sources.
- Support evaluation of grounding, contextualization, explainability, and escalation behavior.
Technical Competencies
- Understanding of agentic architecture and LLM-based applications.
- Expertise in RAG and GraphRAG.
- Knowledge of data and quality governance.
- Experience with prompt engineering, agent workflows, and agent orchestration.
- Experience with ITSM tools such as ServiceNow, Jira, or equivalent.
- Experience with APIs, support workflows, automation, dashboards, KPIs, classification, clustering, and tagging.
Qualifications
- 10–12 years of experience are specified for the role.
- Graduate-level education or above is specified.
- Required knowledge includes agentic architecture, RAG/GraphRAG, data and quality governance, intercultural communication, and BNP context.
- Behavioral expectations include excellent communication, teamwork, change support, attention to detail, and rigor.
Key Deliverables
Knowledge ingestion pipelines, RAG-ready datasets, GraphRAG-ready structures, metadata models, and chunking and tagging standards.
Source certification registries, quality dashboards, automated control scripts, graph mappings, evaluation datasets, and traceability reports.
Monitoring for knowledge refresh and obsolete, duplicate, or contradictory content.
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