Solution Architect - Agentic AI Data Architect
Company : Diverse Lynx
Location : Atlanta, GA, 30303
Posted Date : 28 October 2025
Job Type : Full Time
Category : Software Development
Occupation : Data Architect
Job Details
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:
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.
- 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).
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