About the job:

Experience: 8 to 15 years only.

Location: Hyderabad/Bangalore/Chennai.

Notice period: Immediate to 60 days.

Education: Only BTech/MTech.

We are seeking an experienced Data Architect with strong expertise in Data Engineering, Retrieval-Augmented Generation (RAG) pipelines, and Agentic AI platforms.

The ideal candidate will be responsible for designing and implementing scalable data architectures that support GenAI, Agentic AI, and advanced analytics use cases. This role requires deep knowledge of modern data platforms, vector databases, knowledge graphs, data pipelines, and AI-driven application architectures.

Key Responsibilities :

- Design and develop scalable data architectures to support GenAI and Agentic AI platforms.

- Architect and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise AI applications.

- Build and manage data ingestion, transformation, and orchestration frameworks for structured and unstructured data.

- Design and implement vector databases, embedding pipelines, metadata management, and semantic search capabilities.

- Collaborate with AI/ML engineers to integrate LLMs, RAG frameworks, and agent orchestration platforms.

- Define data governance, security, lineage, and compliance standards across AI-driven solutions.

- Develop data models and knowledge repositories to support intelligent agents and decision-making systems.

- Design real-time and batch data processing architectures using cloud-native technologies.

- Evaluate emerging technologies in GenAI, Agentic AI, vector search, and data engineering.

- Provide architectural guidance and technical leadership to engineering teams.

Required Skills & Qualifications :

- Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field.

- 10+ years of experience in Data Architecture and Data Engineering.

- Strong experience designing enterprise-scale data platforms and cloud-native architectures.

- Hands-on experience with RAG architectures, semantic search, and vector databases.

- Expertise in data modeling, data warehousing, ETL/ELT, and data governance.

- Strong knowledge of Python, SQL, Spark, and distributed data processing frameworks.

- Experience with cloud platforms such as AWS, Azure, or Google Cloud.

- Experience with orchestration tools such as Airflow, Dagster, or similar platforms.

- Knowledge of LLM ecosystems and frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph, or similar technologies.

- Experience working with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or Azure AI Search.

- Understanding of API integration, microservices, and event-driven architectures.

Preferred Skills:

- Experience building Agentic AI solutions and autonomous AI workflows.

- Knowledge of Knowledge Graphs, Graph Databases, and Semantic Web technologies.

- Familiarity with MLOps, LLMOps, and AI governance frameworks.

- Experience with enterprise data lakes, lakehouse architectures, and real-time streaming platforms.

- Exposure to AI observability, model monitoring, and prompt engineering.

Key Technologies:

- Python, SQL, Spark.

- Databricks, Snowflake.

- AWS, Azure, GCP.

- LangChain, LangGraph, LlamaIndex.

- CrewAI, AutoGen.

- Pinecone, Weaviate, Milvus, Chroma.

- Kafka, Airflow.

- Vector Search and Semantic Retrieval.

- Knowledge Graphs.

- GenAI and Agentic AI Platforms.

Desired Candidate Profile:

- The ideal candidate should possess a strong blend of Data Architecture, Data Engineering, and AI platform expertise, with the ability to design enterprise-grade RAG and Agentic AI solutions that deliver scalable, secure, and intelligent business outcomes.