TEPHRA
Description:
Role: Solution Architect - Agentic AI Data Architect
Location: NJ, GA, IL or VA
Job Description:
The Agentic AI Data Architect is a specialized role within TCS's AI & Data Business Unit (Americas), focused on designing intelligent data ecosystems that empower autonomous AI agents and multi-agent systems. This role is critical in enabling AI agents to access, retrieve, and reason over structured and unstructured data in real time. The architect will work across industries-BFSI, Manufacturing, Life Sciences, Telecom, Retail, and more-to build knowledge frameworks that support dynamic, context-aware AI behavior.
Key Responsibilities:
• Knowledge Base Architecture: Design and implement data storage and retrieval systems (e.g., vector DBs, graph DBs, time-series DBs) tailored for AI agent consumption.
• Data Modeling for AI Agents: Create domain-specific data models (e.g., knowledge graphs, relational schemas) that align with agent tasks and reasoning needs.
• Memory and State Management: Architect memory systems for agents, including session context, shared memory for multi-agent collaboration, and state persistence.
• RAG Pipeline Design: Build retrieval-augmented generation pipelines, including document ingestion, embedding generation, and query optimization.
• Multi-Modal Data Integration: Enable agents to retrieve and reason across text, images, structured data, and sensor feeds.
• Data Pipeline Coordination: Collaborate with data engineers to define and automate data ingestion, transformation, and refresh processes.
• Performance Optimization: Optimize data access for low-latency retrieval using sharding, caching, and pre-computed embeddings.
• Data Quality & Curation: Establish criteria and processes for filtering, validating, and curating high-quality knowledge sources.
• Industry-Specific Frameworks: Tailor knowledge systems to industry needs (e.g., compliance data in BFSI, biomedical ontologies in Life Sciences).
• Metadata & Ontologies: Define taxonomies, metadata standards, and ontologies to support semantic search and reasoning.
• Collaboration with AI Developers: Align data design with model behavior, fine-tuning datasets, and performance feedback loops.
• Documentation & Governance: Document data architecture and implement governance for content updates, access control, and auditability.
• Ethical Data Use & Privacy: Design systems that respect privacy, anonymize sensitive data, and enforce access permissions.
Qualifications:
• 8+ years of experience in data architecture or engineering, with 2-3 years focused on AI/ML or knowledge systems.
• Expertise in knowledge management, information retrieval, and semantic search.
• Experience with data modeling, ontologies, and knowledge graphs (e.g., RDF, OWL, Neo4j).
• Familiarity with NLP techniques and libraries (spaCy, NLTK, Hugging Face Transformers).
• Hands-on experience with vector databases (Pinecone, Weaviate, FAISS) and graph databases (Neo4j, AWS Neptune).
• Proficiency in data integration, ETL, and scripting (Python, SQL, APIs).
• Experience with search engines (Elasticsearch, Solr) and enterprise search optimization.
• Understanding of embedding generation and dense vector search.
• Strong grasp of data quality, curation, and governance practices.
• Analytical mindset with the ability to abstract complex domains into structured data models.
• Excellent communication skills for working with AI engineers, business stakeholders, and domain experts.
• Detail-oriented with a strong sense of data ethics, privacy, and security.
• Adaptability and eagerness to learn emerging tools (e.g., LlamaIndex, memory architectures).
• Prior experience in AI/ML projects from a data architecture or engineering perspective.
• Familiarity with cloud services for AI data (AWS Kendra, Azure Cognitive Search, GCP Vertex AI).
• Experience with API design, microservices (FastAPI, Flask), and caching (Redis).
• Knowledge of monitoring tools (Kibana, Prometheus, Grafana) and collaboration platforms (Confluence, JIRA).
Salary Range: $158,000 - $210,000 a year
#LI-AD1
Role: Solution Architect - Agentic AI Data Architect
Location: NJ, GA, IL or VA
Job Description:
The Agentic AI Data Architect is a specialized role within TCS's AI & Data Business Unit (Americas), focused on designing intelligent data ecosystems that empower autonomous AI agents and multi-agent systems. This role is critical in enabling AI agents to access, retrieve, and reason over structured and unstructured data in real time. The architect will work across industries-BFSI, Manufacturing, Life Sciences, Telecom, Retail, and more-to build knowledge frameworks that support dynamic, context-aware AI behavior.
Key Responsibilities:
• Knowledge Base Architecture: Design and implement data storage and retrieval systems (e.g., vector DBs, graph DBs, time-series DBs) tailored for AI agent consumption.
• Data Modeling for AI Agents: Create domain-specific data models (e.g., knowledge graphs, relational schemas) that align with agent tasks and reasoning needs.
• Memory and State Management: Architect memory systems for agents, including session context, shared memory for multi-agent collaboration, and state persistence.
• RAG Pipeline Design: Build retrieval-augmented generation pipelines, including document ingestion, embedding generation, and query optimization.
• Multi-Modal Data Integration: Enable agents to retrieve and reason across text, images, structured data, and sensor feeds.
• Data Pipeline Coordination: Collaborate with data engineers to define and automate data ingestion, transformation, and refresh processes.
• Performance Optimization: Optimize data access for low-latency retrieval using sharding, caching, and pre-computed embeddings.
• Data Quality & Curation: Establish criteria and processes for filtering, validating, and curating high-quality knowledge sources.
• Industry-Specific Frameworks: Tailor knowledge systems to industry needs (e.g., compliance data in BFSI, biomedical ontologies in Life Sciences).
• Metadata & Ontologies: Define taxonomies, metadata standards, and ontologies to support semantic search and reasoning.
• Collaboration with AI Developers: Align data design with model behavior, fine-tuning datasets, and performance feedback loops.
• Documentation & Governance: Document data architecture and implement governance for content updates, access control, and auditability.
• Ethical Data Use & Privacy: Design systems that respect privacy, anonymize sensitive data, and enforce access permissions.
Qualifications:
• 8+ years of experience in data architecture or engineering, with 2-3 years focused on AI/ML or knowledge systems.
• Expertise in knowledge management, information retrieval, and semantic search.
• Experience with data modeling, ontologies, and knowledge graphs (e.g., RDF, OWL, Neo4j).
• Familiarity with NLP techniques and libraries (spaCy, NLTK, Hugging Face Transformers).
• Hands-on experience with vector databases (Pinecone, Weaviate, FAISS) and graph databases (Neo4j, AWS Neptune).
• Proficiency in data integration, ETL, and scripting (Python, SQL, APIs).
• Experience with search engines (Elasticsearch, Solr) and enterprise search optimization.
• Understanding of embedding generation and dense vector search.
• Strong grasp of data quality, curation, and governance practices.
• Analytical mindset with the ability to abstract complex domains into structured data models.
• Excellent communication skills for working with AI engineers, business stakeholders, and domain experts.
• Detail-oriented with a strong sense of data ethics, privacy, and security.
• Adaptability and eagerness to learn emerging tools (e.g., LlamaIndex, memory architectures).
• Prior experience in AI/ML projects from a data architecture or engineering perspective.
• Familiarity with cloud services for AI data (AWS Kendra, Azure Cognitive Search, GCP Vertex AI).
• Experience with API design, microservices (FastAPI, Flask), and caching (Redis).
• Knowledge of monitoring tools (Kibana, Prometheus, Grafana) and collaboration platforms (Confluence, JIRA).
Salary Range: $158,000 - $210,000 a year
#LI-AD1