Senior Specialist, Context Engineer

MSD

. Design, build, and govern the enterprise context layer supporting AI, analytics, and decision intelligence .

Posted 11/9/2026full-timeHyderabad • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing semantic models, ontologies, and knowledge graphs to support AI and analytics initiatives. Proficient in establishing enterprise-wide semantic standards and collaborating with cross-functional teams to drive successful AI context layer solutions.

Highest-signal resume keywords

Data ArchitectureKnowledge Graph DevelopmentSemantic TechnologiesGenerative AI ExperienceCross-Functional Collaboration

ATS Keywords

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Hard Skills

Data ArchitectureKnowledge GraphsSemantic ModelsOntology GovernanceData GovernancePythonSQLSPARQLRDFNeo4j

Soft Skills

Excellent CommunicationProblem-SolvingStakeholder CollaborationLeadershipSelf-Starter

Tools & Technologies

Amazon NeptuneGraphDBTigerGraphTimbr.aiOntotextAgile MethodsREST APIsGraphRAG SolutionsSemantic APIsContext-Serving Capabilities

Industry Keywords

AI PlatformsSemantic TechnologiesData ModelingMetadata ManagementBusiness GlossariesMaster Data ManagementData LineageDecision IntelligenceContextual DiscoveryEnterprise AI Initiatives

Tech Stack

Tools & technologies

Neo4jPythonSQL

About the role

Key responsibilities & impact

Design, build, and govern the enterprise context layer supporting AI, analytics, and decision intelligence
Design semantic models, ontologies, taxonomies, business entities, relationships, hierarchies, and process representations
Establish enterprise-wide semantic standards and reusable patterns
Design and implement knowledge graphs and graph schemas supporting reasoning, contextual discovery, querying, and inference
Build AI context layers, GraphRAG solutions, semantic retrieval frameworks, and LLM/autonomous-agent grounding mechanisms
Enable enterprise copilots and AI agents with trusted business context
Support memory and reasoning frameworks for AI systems
Create semantic mappings between business concepts and physical data
Integrate metadata, master data, business glossaries, and ontologies
Build contextual services, semantic APIs, and context-serving capabilities
Facilitate workshops with business stakeholders and subject-matter experts
Translate business understanding into machine-readable semantic models
Establish ontology governance, semantic modeling, and knowledge management standards
Manage lifecycle, quality, and stewardship of enterprise context assets
Collaborate with design, business, and technical leaders from ideation through deployment
Establish technical capabilities across digital teams, manage the backlog, and guide the SteadyState team
Create solution documentation according to system development lifecycle standards
Test IT solutions and analytical capabilities using business use cases, train users, and document results
Perform systems analysis and design on supply chain projects and programs
Participate in evaluations and proof-of-concept activities

Requirements

What you’ll need

Bachelor's degree in business, information systems or related discipline
7+ years in Data Architecture, Information Architecture, Knowledge Engineering, or AI platforms
3+ years building ontologies, semantic models, or knowledge graphs
Experience supporting enterprise AI initiatives
Experience working with cross-functional business and technical teams
Experience using agile methods through POC, MVP, scale-up, and industrialization stages
Ability to work in a global, multi-cultural, English-speaking environment
Ability to work independently and collaboratively with a globally dispersed team
Ability to work with US and EU work hours with necessary overlap
Experience with semantic technologies including RDF, RDFS, OWL, SKOS, SHACL, or SPARQL
Experience with knowledge graph platforms including Neo4j, Amazon Neptune, Stardog, GraphDB, TigerGraph, Timbr.ai, or Ontotext
Knowledge of data modeling, metadata management, business glossaries, master data management, data lineage, and data governance
Experience with Generative AI, LLMs, RAG, GraphRAG, vector databases, semantic search, AI agents, or agent orchestration frameworks
Experience with Python, SQL, Cypher, SPARQL, or REST APIs
Excellent written, verbal communication, and presentation skills
Demonstrated problem-solving, ownership, innovation, and stakeholder collaboration abilities
Proven leadership and people management skills preferred
Self-starter able to work with minimal supervision and interface with mid-level executives
No visa sponsorship available

Benefits

Comp & perks

Hybrid work arrangement
Regular employee status
No travel required
Domestic relocation
Opportunity to work for a global healthcare biopharma company
Professional collaboration across globally distributed technology centers