Description

You will design and implement enterprise-grade data architectures to support GenAI, autonomous agents, and real-time inference.

This role is hybrid.

Responsibilities

• Design data platforms for multi-agent systems, focusing on orchestration, communication, and memory management.
• Architect end-to-end pipelines for AI/ML workloads, including ingestion, feature engineering, and model lifecycle management.
• Implement and scale retrieval-augmented generation (RAG), fine-tuning frameworks, and knowledge graphs in production.
• Develop backend systems for agentic workflows such as task decomposition, context switching, and reinforcement feedback loops.
• Lead data modeling, governance, metadata management, and master data management initiatives.

Required Skills

• 5+ years of experience in data architecture or related roles.
• Hands-on experience with AI/ML frameworks including TensorFlow, PyTorch, and SciKit-learn.
• Proven experience with Generative AI models and LLMs such as GPT, Claude, or LLaMA.
• Proficiency with orchestration frameworks like LangChain, LlamaIndex, or Langgraph.
• Exposure to multi-agent frameworks including CrewAI, AutoGen, ReAct, or CAMEL.
• Knowledge of agentic AI design principles and feedback loop development.
• Understanding of data governance, security, and compliance frameworks like GDPR or HIPAA.
• Any Graduate degree in Computer Science, Data Science, Engineering, or a related field.

Preferred Skills

• Experience building production-grade agentic systems with adaptive learning.
• Familiarity with knowledge graphs, semantic search, and advanced RAG pipelines.
• Cloud or AI/ML certifications such as AWS Certified Data Analytics or Google ML Engineer.

Key Skills

Education

Any Graduate