Diverse Lynx
Solution Architect - Agentic AI Data Architect
Diverse Lynx, Atlanta, Georgia, United States, 30383
Role : Solution Architect - Agentic AI Data Architect
Location : NJ || GA || IL, VA (Onsite)
Full time + benefits
Job Description:
The Agentic AI Data Architect is a specialized role within 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:
rchitect 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:
lign 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 stru ctured 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).
Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.
Location : NJ || GA || IL, VA (Onsite)
Full time + benefits
Job Description:
The Agentic AI Data Architect is a specialized role within 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:
rchitect 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:
lign 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 stru ctured 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).
Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.