Build AWS-based knowledge engineering and AI solutions, including knowledge graphs, ontologies, semantic layers, data pipelines, vector search, LLM grounding, and graph-based applications. The role involves designing, developing, testing, integrating, and deploying scalable components using services such as Neptune, Glue, Lambda, Bedrock, and SageMaker. Candidates collaborate with client and delivery teams, document technical findings, support solutioning and demonstrations, and mentor junior engineers while remaining hands-on.
Project Role : Data Architect
Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.
Must have skills : AWS BigData
Good to have skills : Data Engineering, Graph Databases, Neo4j
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
AI Powered Tech Talent
Role Summary / Description
Engineer role in Knowledge Engineering for a strong individual contributor who helps build scalable Knowledge Graph, semantic layer, ontology, and AI-enabled data solutions using Amazon Web Services (AWS). In this role, the successful candidate works hands-on across the knowledge graph lifecycle from ingestion through modeling, curation, implementation, integration, and deployment. The role applies current methodologies, generative AI, LLM, multimodal, graph, search, and semantic techniques to practical business problems while collaborating with users, use case representatives, engineers, architects, UI designers, and delivery teams. The role must include industry experience or project exposure in domains such as banking, insurance, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences.
Key Responsibilities
Build AWS-based knowledge engineering components using Amazon Neptune, S3, Glue, Lambda, Step Functions, OpenSearch, SageMaker, Bedrock, IAM, CloudWatch, APIs, and other relevant AWS services.
Implement graph ingestion, ontology/schema pipelines, semantic data products, vector and search integrations, LLM grounding layers, and governed access patterns on AWS.
Build Knowledge Graph components that contribute to transforming a client's data architecture.
Design, develop, configure, test, and implement AI and semantic solutions that integrate clearly with the broader enterprise system.
Work alongside project teams, delivery leads, engineers, architects, users, use case representatives, and UI designers to deliver assigned components of an end-to-end solution.
Build solid working relationships with client counterparts on the workstream and communicate implementation progress, risks, dependencies, and technical findings clearly.
Help assemble supporting evidence for recommended semantic layer solutions, including design rationale, implementation notes, validation inputs, data quality observations, and test outcomes.
Support sales, solutioning, demos, accelerators, or reusable assets when called upon by providing technical inputs or implementation examples.
Design, evaluate, maintain, and deploy ontologies, schemas, mappings, graph data models, metadata structures, validation rules, and knowledge graph components as needed.
Keep developing skills in cutting-edge Data and AI solutions, especially agentic technologies, generative AI, LLMs, multimodal models, semantic search, graph RAG, and knowledge graph approaches.
Share learnings with the team and help junior engineers understand practical development patterns, engineering standards, and knowledge graph implementation practices.
Required Qualifications
Bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field.
Minimum 2 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, graph query languages, schema design, ontology management, and KG curation.
Minimum 2 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation.
Minimum 2 years designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical contexts.
Minimum 1 year of experience with end-to-end data pipeline implementation for AI applications, especially LLM-enabled or enterprise knowledge applications, with hands-on design and configuration.
Minimum 2 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases.
This role does not require leadership or commercial ownership team lead exposure is good to have.
Ability to work hands-on as a strong individual contributor while collaborating across project teams and delivery workstreams.
Required Skills/ Experience
Hands-on experience building AWS-enabled knowledge engineering solutions with Amazon Neptune, S3, Glue, Lambda, Step Functions, OpenSearch, SageMaker, Bedrock, IAM, CloudWatch, APIs, and cloud-native data/AI services.
2+ years of Python experience with frameworks and tools such as TensorFlow, PyTorch, PySpark, SQL, SPARQL, SHACL, Apache Airflow, Apache NiFi, APIs, and ETL pipeline development.
Practical experience with NLP and/or search techniques, prompt engineering, LLMs, retrieval-augmented generation, vector search, semantic search, and enterprise-scale AI application patterns.
Ability to formulate real-world problems into practical, efficient, and scalable AI and Knowledge Graph solution components.
Industry experience applying AWS-enabled knowledge engineering solutions in banking, insurance, healthcare, retail, communications, manufacturing, energy, public services, life sciences, or another relevant domain.
Ability to build AWS-based components for semantic layer, ontology, graph, vector search, search, retrieval, and LLM grounding use cases.
Good to Have Skills
2+ years of hands-on experience with cloud platforms, with AWS specialization and exposure to Azure or GCP in multi-cloud environments.
AWS certification or hands-on project experience in AWS data, AI/ML, solution architecture, or cloud engineering.
Team lead exposure or readiness to guide junior engineers on project tasks.
External client-facing consulting experience, including working with client stakeholders, delivery teams, or solutioning teams.
Broad experience in diverse ML techniques, graph RAG, agentic systems, semantic search, entity resolution, multimodal models, explainable AI, and responsible AI practices.
Experience building reusable code components, implementation templates, design notes, demos, proof-of-concept assets, runbooks, or enablement material.
15 years full time education
Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.
Must have skills : AWS BigData
Good to have skills : Data Engineering, Graph Databases, Neo4j
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
AI Powered Tech Talent
Role Summary / Description
Engineer role in Knowledge Engineering for a strong individual contributor who helps build scalable Knowledge Graph, semantic layer, ontology, and AI-enabled data solutions using Amazon Web Services (AWS). In this role, the successful candidate works hands-on across the knowledge graph lifecycle from ingestion through modeling, curation, implementation, integration, and deployment. The role applies current methodologies, generative AI, LLM, multimodal, graph, search, and semantic techniques to practical business problems while collaborating with users, use case representatives, engineers, architects, UI designers, and delivery teams. The role must include industry experience or project exposure in domains such as banking, insurance, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences.
Key Responsibilities
Build AWS-based knowledge engineering components using Amazon Neptune, S3, Glue, Lambda, Step Functions, OpenSearch, SageMaker, Bedrock, IAM, CloudWatch, APIs, and other relevant AWS services.
Implement graph ingestion, ontology/schema pipelines, semantic data products, vector and search integrations, LLM grounding layers, and governed access patterns on AWS.
Build Knowledge Graph components that contribute to transforming a client's data architecture.
Design, develop, configure, test, and implement AI and semantic solutions that integrate clearly with the broader enterprise system.
Work alongside project teams, delivery leads, engineers, architects, users, use case representatives, and UI designers to deliver assigned components of an end-to-end solution.
Build solid working relationships with client counterparts on the workstream and communicate implementation progress, risks, dependencies, and technical findings clearly.
Help assemble supporting evidence for recommended semantic layer solutions, including design rationale, implementation notes, validation inputs, data quality observations, and test outcomes.
Support sales, solutioning, demos, accelerators, or reusable assets when called upon by providing technical inputs or implementation examples.
Design, evaluate, maintain, and deploy ontologies, schemas, mappings, graph data models, metadata structures, validation rules, and knowledge graph components as needed.
Keep developing skills in cutting-edge Data and AI solutions, especially agentic technologies, generative AI, LLMs, multimodal models, semantic search, graph RAG, and knowledge graph approaches.
Share learnings with the team and help junior engineers understand practical development patterns, engineering standards, and knowledge graph implementation practices.
Required Qualifications
Bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field.
Minimum 2 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, graph query languages, schema design, ontology management, and KG curation.
Minimum 2 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation.
Minimum 2 years designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical contexts.
Minimum 1 year of experience with end-to-end data pipeline implementation for AI applications, especially LLM-enabled or enterprise knowledge applications, with hands-on design and configuration.
Minimum 2 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases.
This role does not require leadership or commercial ownership team lead exposure is good to have.
Ability to work hands-on as a strong individual contributor while collaborating across project teams and delivery workstreams.
Required Skills/ Experience
Hands-on experience building AWS-enabled knowledge engineering solutions with Amazon Neptune, S3, Glue, Lambda, Step Functions, OpenSearch, SageMaker, Bedrock, IAM, CloudWatch, APIs, and cloud-native data/AI services.
2+ years of Python experience with frameworks and tools such as TensorFlow, PyTorch, PySpark, SQL, SPARQL, SHACL, Apache Airflow, Apache NiFi, APIs, and ETL pipeline development.
Practical experience with NLP and/or search techniques, prompt engineering, LLMs, retrieval-augmented generation, vector search, semantic search, and enterprise-scale AI application patterns.
Ability to formulate real-world problems into practical, efficient, and scalable AI and Knowledge Graph solution components.
Industry experience applying AWS-enabled knowledge engineering solutions in banking, insurance, healthcare, retail, communications, manufacturing, energy, public services, life sciences, or another relevant domain.
Ability to build AWS-based components for semantic layer, ontology, graph, vector search, search, retrieval, and LLM grounding use cases.
Good to Have Skills
2+ years of hands-on experience with cloud platforms, with AWS specialization and exposure to Azure or GCP in multi-cloud environments.
AWS certification or hands-on project experience in AWS data, AI/ML, solution architecture, or cloud engineering.
Team lead exposure or readiness to guide junior engineers on project tasks.
External client-facing consulting experience, including working with client stakeholders, delivery teams, or solutioning teams.
Broad experience in diverse ML techniques, graph RAG, agentic systems, semantic search, entity resolution, multimodal models, explainable AI, and responsible AI practices.
Experience building reusable code components, implementation templates, design notes, demos, proof-of-concept assets, runbooks, or enablement material.
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.
Accenture Chennai, Tamil Nadu, IND Office
Chennai, India
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To locals, it's no secret that South India is leading the charge in big data infrastructure. While the environmental impact of data centers has long been a concern, emerging hubs like Chennai are favored by companies seeking ready access to renewable energy resources, which provide more sustainable and cost-effective solutions. As a result, Chennai, along with neighboring Bengaluru and Hyderabad, is poised for significant growth, with a projected 65 percent increase in data center capacity over the next decade.
