- Location
- Hyderabad, HDC4A, India
- Type
- Full-time
- Department
- Engineering
- Experience
- 12+ years
- Source
- Workday
Description
Project Role : Packaged/SaaS Application Engineer
Project Role Description : Configure and support packaged or SaaS applications to adapt features, manage releases, and ensure system stability. Use standard tools, APIs, and low-code platforms to align solutions with business needs while preserving compatibility and performance.
Must have skills : Generative AI
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
ROLE OVERVIEW
This position carries principal technical authority over a strategic enterprise initiative focused on building a Semantic Knowledge Graph platform across a large and complex data governance estate. The incumbent will lead the end-to-end engineering lifecycle: from extracting structured and unstructured metadata out of the enterprise data catalog platform, through transformation and loading into a cloud-native graph database, to the design and delivery of the AI agent layer that converts governed graph knowledge into auditable, business-consumable intelligence.
The role demands equal competence in hands-on engineering and senior stakeholder engagement. The incumbent is expected to make architectural decisions, resolve technical ambiguities, and deliver working software. Close and continuous collaboration with Business Analysts, domain data stewards, ontology specialists, and enterprise architects is a core expectation of this position.
KEY RESPONSIBILITIES
Architecture and Technical Leadership
Provide end-to-end technical ownership of the Semantic Knowledge Graph platform, spanning metadata ingestion, graph modelling, AI agent engineering, and platform observability.
Define and govern the graph data model — entities, relationships, properties, and ontology alignment — to accurately represent clinical, regulatory, commercial, and operational data assets.
Establish and enforce engineering standards across the agent layer: prompt engineering governance, hallucination mitigation strategies, tool failure handling, context window management, and response provenance tracing.
Contribute to enterprise architecture decisions on semantic interoperability, data contracts, metadata standards, and platform reuse across program workstreams.
Metadata Extraction and Graph Pipeline Engineering
Lead the design and implementation of metadata extraction pipelines from Collibra, covering structured catalog assets — lineage, classifications, business glossary, data quality rules, and workflow objects — as well as unstructured artefacts including descriptions, policies, annotations, and stewardship notes.
Architect the transformation and loading framework into Amazon Neptune, with explicit attention to incremental load strategies, schema evolution, data quality at ingestion, and full auditability of provenance.
Define standards for graph serialisation formats, bulk load procedures, and streaming upsert patterns suitable for enterprise-scale operational use.
AI Agent Engineering
Lead the engineering of LLM-powered, tool-calling AI agents capable of multi-hop graph traversal, hybrid vector-graph retrieval, and synthesised natural-language response generation.
Design agent tool definitions, retrieval chains, and response contracts aligned to data governance, clinical, regulatory, and commercial analytics use cases.
Establish evaluation frameworks for agent response quality, semantic correctness, answer provenance, and operational reliability.
Stakeholder Collaboration and Delivery
Partner with Business Analysts through discovery, sprint, and validation cycles — translating use case narratives into precise graph query patterns, agent behaviors, and response contracts.
Present architectural decisions and tradeoff analyses to CDO, CTO, and enterprise architecture-level audiences.
Provide technical mentorship and engineering guidance to the AI Engineering Analyst within this hiring cohort.
Maintain platform observability covering pipeline lineage, agent trace logging, latency profiling, cost attribution, and graph coverage gap analysis.
REQUIRED EXPERIENCE & QUALIFICATIONS
12 to 15 years of progressive experience in data engineering, platform engineering, or AI engineering, with demonstrable senior technical leadership across the latter portion of that career.
Substantial hands-on experience with graph databases in production environments — Amazon Neptune is strongly preferred Neo4j or TigerGraph are acceptable — including data modelling, query optimisation, and operational management at enterprise scale.
Deep working knowledge of Collibra Data Intelligence Cloud: REST and GraphQL API extraction, business glossary, lineage, data quality modules, classifications, and workflow objects at a practitioner level.
Production-grade experience architecting and delivering LLM-powered agentic systems that have operated under real load, handled real failures, and been validated by real users.
Proficiency in Python at a software engineering standard strong command of Gremlin and/or SPARQL.
AWS platform fluency: Neptune, S3, Glue, Lambda, Step Functions, OpenSearch, and Amazon Bedrock.
Demonstrated domain depth in Healthcare or Life Sciences — clinical data governance, regulatory submissions data, pharmacovigilance, or pharma commercial analytics — at a level that supports credible engagement with domain subject matter experts without extended onboarding.
Established record of senior stakeholder engagement: presenting architecture decisions, managing technical tradeoffs transparently, and influencing cross-functional teams without direct authority.
DESIRABLE
Formal ontology engineering experience: OWL, SKOS, RDF, and working knowledge of domain-standard clinical and regulatory vocabularies.
Experience with Collibra Protect, Data Quality, or Workflow Automation modules.
Familiarity with graph machine learning techniques for knowledge graph enrichment, including node classification and link prediction.
Experience designing semantic interoperability interfaces: SPARQL endpoints, ontology APIs, or metadata exchange standards.
Prior enterprise data governance program leadership in a pharmaceutical, biotech, or medical device context.
15 years full time education
Project Role Description : Configure and support packaged or SaaS applications to adapt features, manage releases, and ensure system stability. Use standard tools, APIs, and low-code platforms to align solutions with business needs while preserving compatibility and performance.
Must have skills : Generative AI
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
ROLE OVERVIEW
This position carries principal technical authority over a strategic enterprise initiative focused on building a Semantic Knowledge Graph platform across a large and complex data governance estate. The incumbent will lead the end-to-end engineering lifecycle: from extracting structured and unstructured metadata out of the enterprise data catalog platform, through transformation and loading into a cloud-native graph database, to the design and delivery of the AI agent layer that converts governed graph knowledge into auditable, business-consumable intelligence.
The role demands equal competence in hands-on engineering and senior stakeholder engagement. The incumbent is expected to make architectural decisions, resolve technical ambiguities, and deliver working software. Close and continuous collaboration with Business Analysts, domain data stewards, ontology specialists, and enterprise architects is a core expectation of this position.
KEY RESPONSIBILITIES
Architecture and Technical Leadership
Provide end-to-end technical ownership of the Semantic Knowledge Graph platform, spanning metadata ingestion, graph modelling, AI agent engineering, and platform observability.
Define and govern the graph data model — entities, relationships, properties, and ontology alignment — to accurately represent clinical, regulatory, commercial, and operational data assets.
Establish and enforce engineering standards across the agent layer: prompt engineering governance, hallucination mitigation strategies, tool failure handling, context window management, and response provenance tracing.
Contribute to enterprise architecture decisions on semantic interoperability, data contracts, metadata standards, and platform reuse across program workstreams.
Metadata Extraction and Graph Pipeline Engineering
Lead the design and implementation of metadata extraction pipelines from Collibra, covering structured catalog assets — lineage, classifications, business glossary, data quality rules, and workflow objects — as well as unstructured artefacts including descriptions, policies, annotations, and stewardship notes.
Architect the transformation and loading framework into Amazon Neptune, with explicit attention to incremental load strategies, schema evolution, data quality at ingestion, and full auditability of provenance.
Define standards for graph serialisation formats, bulk load procedures, and streaming upsert patterns suitable for enterprise-scale operational use.
AI Agent Engineering
Lead the engineering of LLM-powered, tool-calling AI agents capable of multi-hop graph traversal, hybrid vector-graph retrieval, and synthesised natural-language response generation.
Design agent tool definitions, retrieval chains, and response contracts aligned to data governance, clinical, regulatory, and commercial analytics use cases.
Establish evaluation frameworks for agent response quality, semantic correctness, answer provenance, and operational reliability.
Stakeholder Collaboration and Delivery
Partner with Business Analysts through discovery, sprint, and validation cycles — translating use case narratives into precise graph query patterns, agent behaviors, and response contracts.
Present architectural decisions and tradeoff analyses to CDO, CTO, and enterprise architecture-level audiences.
Provide technical mentorship and engineering guidance to the AI Engineering Analyst within this hiring cohort.
Maintain platform observability covering pipeline lineage, agent trace logging, latency profiling, cost attribution, and graph coverage gap analysis.
REQUIRED EXPERIENCE & QUALIFICATIONS
12 to 15 years of progressive experience in data engineering, platform engineering, or AI engineering, with demonstrable senior technical leadership across the latter portion of that career.
Substantial hands-on experience with graph databases in production environments — Amazon Neptune is strongly preferred Neo4j or TigerGraph are acceptable — including data modelling, query optimisation, and operational management at enterprise scale.
Deep working knowledge of Collibra Data Intelligence Cloud: REST and GraphQL API extraction, business glossary, lineage, data quality modules, classifications, and workflow objects at a practitioner level.
Production-grade experience architecting and delivering LLM-powered agentic systems that have operated under real load, handled real failures, and been validated by real users.
Proficiency in Python at a software engineering standard strong command of Gremlin and/or SPARQL.
AWS platform fluency: Neptune, S3, Glue, Lambda, Step Functions, OpenSearch, and Amazon Bedrock.
Demonstrated domain depth in Healthcare or Life Sciences — clinical data governance, regulatory submissions data, pharmacovigilance, or pharma commercial analytics — at a level that supports credible engagement with domain subject matter experts without extended onboarding.
Established record of senior stakeholder engagement: presenting architecture decisions, managing technical tradeoffs transparently, and influencing cross-functional teams without direct authority.
DESIRABLE
Formal ontology engineering experience: OWL, SKOS, RDF, and working knowledge of domain-standard clinical and regulatory vocabularies.
Experience with Collibra Protect, Data Quality, or Workflow Automation modules.
Familiarity with graph machine learning techniques for knowledge graph enrichment, including node classification and link prediction.
Experience designing semantic interoperability interfaces: SPARQL endpoints, ontology APIs, or metadata exchange standards.
Prior enterprise data governance program leadership in a pharmaceutical, biotech, or medical device context.
15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Skills
PythonAWSMachine LearningData EngineeringRESTGraphQL
