TEPHRA
Description:
Role: Agentic AI Data Architect
Location options: San Francisco Bay Area, New York / New Jersey, Atlanta, Chicago, and Dallas.
Preface
The Agentic AI Data Architect is a specialized role in TCS's AI & Data Business Unit (Americas) that focuses on structuring and preparing data specifically for AI agents and multi-agent systems. This role involves designing how information is stored, indexed, and retrieved to support capabilities like dynamic reasoning, memory, and context for AI agents. Working in a hybrid model from TCS hubs, you will engage with clients across BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods to build industry-tailored data solutions. Your designs will directly impact how well AI agents can understand their environment and make informed decisions, making you a key enabler of grounded, context-aware AI.
What You Would Be Doing
•Knowledge Base Architecture: Design the data and knowledge base architecture for AI agents, including choosing the right type of storage for different data needs and defining how data is organized (schemas, ontologies) for effective querying.
•Data Modeling for AI Agents: Develop data models representing domain knowledge required by AI agents, facilitating the kinds of questions or tasks the agent will handle.
•Memory and State Management: Architect how AI agents store and update their state or memory, ensuring consistency and isolation of context between different sessions or agent instances.
•Retrieval-Augmented Generation (RAG) Pipelines: Plan and design the pipeline for retrieval-augmented generation, outlining how new data gets processed and included to keep the agent's knowledge current.
•Multi-Modal Data Integration: Design an integrated data approach for agents working with multiple data modalities, allowing retrieval from various sources.
•Data Pipeline Coordination: Work with Data Engineering teams to establish pipelines that feed the agent's data stores, ensuring automated processes populate and refresh the data architecture.
•Performance Optimization for Retrieval: Ensure efficient data access for agents by analyzing query patterns and optimizing storage or retrieval mechanisms.
•Data Quality and Curation: Develop approaches for curating and filtering data, maintaining a high-quality knowledge corpus.
•Industry-Specific Knowledge Frameworks: Tailor knowledge architecture to industry needs, understanding domain data and shaping it into a form usable by AI agents.
•Metadata and Ontologies: Define metadata standards and ontologies for the agent's knowledge, improving the agent's ability to fetch relevant info and do basic reasoning.
•Collaboration with AI Developers: Work closely with AI engineers and data scientists to align data usage with model behavior, adjusting data design based on AI performance metrics.
•Documentation and Governance: Document the data architecture for AI agents and implement governance around the knowledge base.
•Ethical Data Use & Privacy: Ensure the design respects privacy and ethical considerations, handling removal requests and segregating indexes as needed.
What Skills Are Expected
•Knowledge Management & Retrieval Expertise: Strong understanding of information retrieval concepts, search engines, and knowledge management systems.
•Data Modeling & Ontologies: Skills in data modeling for knowledge representation, including experience with knowledge graphs and ontologies.
•NLP and Unstructured Data Handling: Familiarity with natural language processing techniques for processing unstructured text.
•Vector Databases & Semantic Search: Experience with semantic search and vector databases.
•Graph Database Knowledge: Knowledge of graph databases and query languages.
•Data Integration & ETL: Foundation in data integration, including light data engineering.
•Data Quality & Curation Mindset: Keen sense for data quality issues and designing measures to maintain quality.
•Analytical Thinking: Strong analytical and problem-solving skills around data.
•Collaboration & Communication: Ability to communicate with both technical and non-technical stakeholders.
•Attention to Detail: Meticulous oversight ensuring metadata is correctly applied and sources are well-documented.
•Adaptability & Continuous Learning: Eagerness to keep up with research and emerging tools.
•Domain Knowledge Flexibility: Ability to quickly learn the basics of various domains.
•Experience with AI Projects: Prior experience working on AI or advanced analytics projects from the data side.
•Ethical and Secure Data Handling: Knowledge of privacy laws and ethical guidelines, ensuring secure data handling.
Key Technology Capabilities
•Search Engines & Indexing: Proficiency with search technologies like Elasticsearch or Solr.
•Vector Search & LLM Integration: Hands-on experience with vector databases or libraries for embedding-based search.
•Graph Databases: Comfortable with graph databases and query languages.
•Data Pipelines & ETL Tools: Knowledge of data pipeline tools for ingesting and updating knowledge stores.
•NLP Toolkits: Familiarity with NLP libraries for text preprocessing tasks.
•Database Management: Skills in managing and querying SQL/NoSQL databases.
•Cloud Services for AI Data: Familiarity with cloud services geared towards search and AI data.
•Scripting and Programming: Strong scripting skills in Python or other relevant languages.
•API & Microservices: Ability to design or use APIs for data retrieval.
•Caching & In-Memory Stores: Knowledge of caching mechanisms to speed up frequent queries.
•Data Analytics Tools: Ability to use analytics or BI tools to examine data.
•Quality and Annotation Tools: Familiarity with tools for data annotation or curation.
•Monitoring & Logging: Setting up monitoring for data pipelines and knowledge base health.
•Security & Access Control: Use of security features in data stores.
•Collaboration Tools: Proficiency with documentation and task tracking tools.
•AI/ML Basics: Understanding how models use data.
•Domain-Specific Platforms: Knowledge of domain-specific data platforms.
Salary Range: $158,000 - $210,000 a year
#LI-AD1
Role: Agentic AI Data Architect
Location options: San Francisco Bay Area, New York / New Jersey, Atlanta, Chicago, and Dallas.
Preface
The Agentic AI Data Architect is a specialized role in TCS's AI & Data Business Unit (Americas) that focuses on structuring and preparing data specifically for AI agents and multi-agent systems. This role involves designing how information is stored, indexed, and retrieved to support capabilities like dynamic reasoning, memory, and context for AI agents. Working in a hybrid model from TCS hubs, you will engage with clients across BFSI, Manufacturing, Life Sciences, Telecom, Retail, Travel, and Consumer Goods to build industry-tailored data solutions. Your designs will directly impact how well AI agents can understand their environment and make informed decisions, making you a key enabler of grounded, context-aware AI.
What You Would Be Doing
•Knowledge Base Architecture: Design the data and knowledge base architecture for AI agents, including choosing the right type of storage for different data needs and defining how data is organized (schemas, ontologies) for effective querying.
•Data Modeling for AI Agents: Develop data models representing domain knowledge required by AI agents, facilitating the kinds of questions or tasks the agent will handle.
•Memory and State Management: Architect how AI agents store and update their state or memory, ensuring consistency and isolation of context between different sessions or agent instances.
•Retrieval-Augmented Generation (RAG) Pipelines: Plan and design the pipeline for retrieval-augmented generation, outlining how new data gets processed and included to keep the agent's knowledge current.
•Multi-Modal Data Integration: Design an integrated data approach for agents working with multiple data modalities, allowing retrieval from various sources.
•Data Pipeline Coordination: Work with Data Engineering teams to establish pipelines that feed the agent's data stores, ensuring automated processes populate and refresh the data architecture.
•Performance Optimization for Retrieval: Ensure efficient data access for agents by analyzing query patterns and optimizing storage or retrieval mechanisms.
•Data Quality and Curation: Develop approaches for curating and filtering data, maintaining a high-quality knowledge corpus.
•Industry-Specific Knowledge Frameworks: Tailor knowledge architecture to industry needs, understanding domain data and shaping it into a form usable by AI agents.
•Metadata and Ontologies: Define metadata standards and ontologies for the agent's knowledge, improving the agent's ability to fetch relevant info and do basic reasoning.
•Collaboration with AI Developers: Work closely with AI engineers and data scientists to align data usage with model behavior, adjusting data design based on AI performance metrics.
•Documentation and Governance: Document the data architecture for AI agents and implement governance around the knowledge base.
•Ethical Data Use & Privacy: Ensure the design respects privacy and ethical considerations, handling removal requests and segregating indexes as needed.
What Skills Are Expected
•Knowledge Management & Retrieval Expertise: Strong understanding of information retrieval concepts, search engines, and knowledge management systems.
•Data Modeling & Ontologies: Skills in data modeling for knowledge representation, including experience with knowledge graphs and ontologies.
•NLP and Unstructured Data Handling: Familiarity with natural language processing techniques for processing unstructured text.
•Vector Databases & Semantic Search: Experience with semantic search and vector databases.
•Graph Database Knowledge: Knowledge of graph databases and query languages.
•Data Integration & ETL: Foundation in data integration, including light data engineering.
•Data Quality & Curation Mindset: Keen sense for data quality issues and designing measures to maintain quality.
•Analytical Thinking: Strong analytical and problem-solving skills around data.
•Collaboration & Communication: Ability to communicate with both technical and non-technical stakeholders.
•Attention to Detail: Meticulous oversight ensuring metadata is correctly applied and sources are well-documented.
•Adaptability & Continuous Learning: Eagerness to keep up with research and emerging tools.
•Domain Knowledge Flexibility: Ability to quickly learn the basics of various domains.
•Experience with AI Projects: Prior experience working on AI or advanced analytics projects from the data side.
•Ethical and Secure Data Handling: Knowledge of privacy laws and ethical guidelines, ensuring secure data handling.
Key Technology Capabilities
•Search Engines & Indexing: Proficiency with search technologies like Elasticsearch or Solr.
•Vector Search & LLM Integration: Hands-on experience with vector databases or libraries for embedding-based search.
•Graph Databases: Comfortable with graph databases and query languages.
•Data Pipelines & ETL Tools: Knowledge of data pipeline tools for ingesting and updating knowledge stores.
•NLP Toolkits: Familiarity with NLP libraries for text preprocessing tasks.
•Database Management: Skills in managing and querying SQL/NoSQL databases.
•Cloud Services for AI Data: Familiarity with cloud services geared towards search and AI data.
•Scripting and Programming: Strong scripting skills in Python or other relevant languages.
•API & Microservices: Ability to design or use APIs for data retrieval.
•Caching & In-Memory Stores: Knowledge of caching mechanisms to speed up frequent queries.
•Data Analytics Tools: Ability to use analytics or BI tools to examine data.
•Quality and Annotation Tools: Familiarity with tools for data annotation or curation.
•Monitoring & Logging: Setting up monitoring for data pipelines and knowledge base health.
•Security & Access Control: Use of security features in data stores.
•Collaboration Tools: Proficiency with documentation and task tracking tools.
•AI/ML Basics: Understanding how models use data.
•Domain-Specific Platforms: Knowledge of domain-specific data platforms.
Salary Range: $158,000 - $210,000 a year
#LI-AD1