Data Architect

9 hours ago

Alleppey, Kerala, India beBeeData Full time ₹ 1,80,00,000 - ₹ 2,50,00,000
Key Responsibilities:
We are seeking an experienced Data Architect to design and implement scalable AI systems leveraging Large Language Models (LLMs), Generative AI, and multi-agent frameworks to support diverse enterprise use cases. This includes creating retrieval-augmented generation (RAG) pipelines integrating vector databases, LLMs, and proprietary knowledge bases.

Agentic Workflows:
The ideal candidate will build robust agentic workflows using tools like LangGraph, CrewAI, or Agno, enabling autonomous task execution, planning, memory, and tool use.

Achievements in Data Science Field:
We are looking for someone who has developed and deployed LLM-based solutions at scale, including fine-tuning, prompt engineering, and RAG-based systems. The successful candidate will also have demonstrated expertise in model lifecycle management, MLOps, and deployment workflows (batch, real-time, streaming). Additionally, they will have fostered collaboration with product, engineering, and data teams to translate business requirements into architectural blueprints and technical roadmaps.

Technical Skills and Tools:
The following skills and tools are required:
  • Languages: Python, SQL, Bash
  • ML/AI Frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers.
  • GenAI & LLM Tooling: OpenAI APIs, LangChain, LlamaIndex, Cohere, Claude, Azure OpenAI.
  • Agentic & Multi-Agent Frameworks: LangGraph, CrewAI, Agno, AutoGen
  • Search & Retrieval: FAISS, Pinecone, Weaviate, Elasticsearch
  • Cloud Platforms: AWS, GCP, Azure (preferred: Vertex AI, SageMaker, Bedrock)
  • MLOps & DevOps: MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines, Terraform, FAST API
  • Data Tools: Snowflake, BigQuery, Spark, Airflow.


Requirements and Qualifications:
The ideal candidate will have 9+ years of experience in AI/ML engineering or data science, with 2+ years in AI architecture or system design. They will also have a strong understanding of agentic AI design principles, multi-agent orchestration, and tool-augmented LLMs. Additionally, they will have proficiency with cloud-native ML/AI services and infrastructure design across AWS, GCP, or Azure. Familiarity with data governance, AI ethics, and security considerations in production-grade systems is also required. Finally, excellent communication and leadership skills, with the ability to influence technical and business stakeholders, are essential for this role.

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