Data AI Architect
Data AI Architect
HCL TechBee14-16 Years
- Posted 22 hours ago
- Be among the first 10 applicants
Job Description
Location: Bangalore /Pune/ Hyderabad /Chennai
Required Skills & Experience
- 14+ years of overall experience in data architecture, data engineering, or related roles.
- 8+ years of hands-on experience with cloud data platforms (AWS, Azure, and/or GCP) — architecture, design, and implementation.
- Hands-on experience with Databricks and/or Snowflake for large-scale data engineering, analytics, and Lakehouse workloads.
- Proven hands-on experience with AI/ML solutions, including LLM-based application development and AI agent design/development.
- Strong grounding in data modelling (conceptual, logical, physical), data warehousing, and modern Lakehouse architectures.
- Experience with data integration tools/ETL-ELT frameworks (e.g., Informatica, dbt, Talend, Glue, ADF).
- Working knowledge of vector databases (e.g., Pinecone, FAISS, vector) and RAG architectures.
- Experience with agent frameworks/protocols (e.g., Lang Chain, Lang Graph, AutoGen, MCP) is highly desirable.
- Proficiency in SQL and at least one programming language (Python preferred) for prototyping and integration work.
- Solid understanding of data governance, security, and privacy/compliance requirements (e.g., GDPR).
- Strong stakeholder management, communication, and technical leadership skills.
Preferred Qualifications
- Cloud certifications (AWS/Azure/GCP Solutions Architect or Data Engineer).
- Databricks (e.g., Databricks Certified Data Engineer) and/or Snowflake (e.g., SnowPro Core) certifications.
- Experience in a specific domain relevant to the business (e.g., banking, financial services, insurance).
- Exposure to MLOps/LLMOps tooling (MLflow, Vertex AI, SageMaker, Azure AI Studio).
- Experience presenting architecture and AI strategy to senior/executive stakeholders.
More Info
Key Skills
AI ML solutions
AutoGen MCP
Lang Graph
Lang Chain
vector databases
Pinecone
AI agent design development
ETL-ELT frameworks
Lakehouse architectures
FAISS
cloud data platforms
LLM-based application development
About Company
Skills:
snowflake , Metadata Management, Google Cloud Platform, Data Quality, MLops, Databricks, Microsoft Azure, Data Governance, AWS, GenAI, LLMs, AI Agents, AI governance frameworks, cloud-native architecture patterns, Analytics, Enterprise Data Platforms, Synapse, DataOps, MDM, Microsoft Fabric, AI solutions, RAG architectures
Skills:
snowflake , Terraform, Docker, Python, Sql, Devops, Git, MLops, Spark, AWS CloudFormation, Databricks, Azure, Kubernetes, AI Architecture, Hugging Face, MLflow, Pinecone, Azure OpenAI, GCP Vertex AI, ChromaDB, LangChain, Generative AI, LLMOps, Synapse, Agentic AI, AWS SageMaker, Azure AI, OpenAI
Skills:
Pyspark, Databricks, Data Architecture, Machine Learning, MLops, Kubernetes, Azure, Docker, LLMs, AI Architecture, MLflow, Azure OpenAI, CI CD, RAG, Agentic AI, Generative AI
Skills:
data quality assurance , object storage , Kafka, Azure DevOps, Azure, Jenkins, Spark, AWS Bedrock, Feast, distributed query engines, OpenAI, Amazon Deequ, Data Architecture for AI, cloud native ETL, DataHub, Apache Atlas, Power BI with Copilot, LangChain, CrewAI, data lakehouses, LangGraph, GitHub Actions, ELT frameworks, platform integration, Vertex AI, Great Expectations, Google ADK, Evidently, AutoGen
Skills:
Azure Machine Learning AML, Retrieval-Augmented Generation RAG, Microsoft Fabric Azure Synapse, Azure OpenAI Service, Azure API Management APIM, Infrastructure as Code Terraform Bicep, Semantic Kernel, AKS Azure Kubernetes Service or Azure Container Apps, Multi-Agent Systems Agentic AI, Azure AI Foundry Agent Service Prompt Flow Model Catalog, Azure AI Content Safety, CI CD using GitHub Actions or Azure DevOps, Cosmos DB including Vector Search, Azure AI Search Vector Hybrid Search, LLM Application Architecture