Cloud Data Architect (With AI experience)

Requirements

• - 15+ years of hands-on data engineering and architecture experience, with 3-5+ years building production AI/ML and LLM-era data infrastructure.
• - Proven experience designing enterprise-scale AI data platforms that serve multiple AI consumers -not just one application or pipeline.
• - Deep expertise in lakehouse and data mesh architectures: Databricks, Delta Lake, PySpark, Kafka, Spark Structured Streaming, cloud-native data services (AWS, Azure).
• - Hands-on experience with vector stores, semantic models, knowledge graphs, and retrieval infrastructure in production environments.
• - Working knowledge of LLMOps: model serving pipelines, MLflow, CI/CD for AI, automated evaluation, and production monitoring.
• - Strong background in data governance, security, and compliance in regulated industries (financial services, payments, cybersecurity, healthcare).
• - Experience defining data access controls for AI agents and automated systems - not just human users.
• Technical Skills
• Primary Skills: Python, SQL, PySpark, Kafka, Databricks, Delta Lake, Snowflake,AWS (S3, Glue, EKS, Bedrock, Kinesis, Redshift), Docker, Kubernetes, Terraform, GitHub Actions, LangChain, LlamaIndex, LLM APIs (OpenAI, AWS Bedrock, Claude, HuggingFace), vector databases (Pinecone, FAISS, ChromaDB, OpenSearch), knowledge graphs (Neo4j).
• Secondary Skills: MLflow, FastAPI, CI/CD pipelines, observability tooling (CloudWatch, Grafana, or equivalent), data lineage and metadata management platforms.

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Company Intel

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