Data Architect – GenAI

Job Responsibilities

• Design and implement enterprise-grade data architectures.
• Lead data modeling, governance, metadata management, data lineage, and master data management initiatives.
• Define scalable data solutions to support real-time inference, and autonomous agent systems.
• Architect and deploy end-to-end pipelines that support AI/ML workloads, including data ingestion, feature engineering, and model lifecycle management.
• Collaborate with AI research and product teams to operationalize GenAI models (e.g., LLMs, SLM and knowledge graphs) and integrate them into business workflows.
• Implement and scale retrieval-augmented generation (RAG) and fine-tuning frameworks in production environments, as well as knowledge graphs.
• Multi-Agent & Agentic AI Systems
• Design data platforms that support multi-agent systems, ensuring seamless orchestration, communication, and memory management among agents.
• Architect data flow and backend systems to support agentic workflows such as task decomposition, context switching, and reinforcement feedback loops along with knowledge graphs.
• Leverage frameworks like LangChain, Langgraph, AutoGen, CrewAI, or similar to build and manage autonomous and collaborative agents.
• Must have exposure to feedback loop design and development for Multiagent or agentic frameworks.

Key skills you will require:

Primary Skills

• Bachelor’s or master’s degree in computer science, Data Science, Engineering, or related field.
• Strong hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Proven track record of working with Generative AI models, LLMs (e.g., GPT, Claude, LLaMA), and orchestration frameworks (LangChain, LlamaIndex, Langgraph).
• Knowledge and exposure to multi-agent frameworks (e.g., CrewAI, AutoGen, ReAct, CAMEL) and agentic AI design principles.
• Solid understanding of data governance, security, and compliance frameworks (GDPR, HIPAA, etc.).
• Excellent communication, leadership, and stakeholder management skills.

 Preferred Qualifications:

• Experience building production-grade agentic systems with adaptive learning and decision-making capabilities.
• Familiarity with knowledge graphs, semantic search, and advanced RAG pipelines.
• Certifications in cloud platforms or AI/ML specializations (e.g., AWS Certified Data Analytics, Google ML Engineer)