Job description

Job Title – AI Data Engineering Lead/ Manager Location – Remote Duration: Long Term contract Job responsibilities: • Looking for AI Data Clienthitect to design, build, govern, and evolve the single source of truth that powers every AI initiative in our organization. • This platform will serve as the foundational nervous system for conversational AI assistants, dashboard intelligence, autonomous AI agents, RAG-powered applications, predictive ML models, and any AI product we build today or in the future. • The resource will Clienthitect the system, drive implementation, own the data contracts that agents and AI applications depend on, enforce security and access governance for both human and agent consumers, and continuously monitor and improve the accuracy and reliability of AI outputs that flow from this platform. Must-Have Experience • 15+ years of hands-on data engineering and Clienthitecture 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 Clienthitectures: Databricks, Delta Lake, PySpark, Kafka, Spark Structured Streaming, cloud-native data services (AWS, Clienture). • 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).

Requirements

Must-haves

Job Title – AI Data Engineering Lead/ Manager
Location – Remote
Duration: Long Term contract
Looking for AI Data Clienthitect to design, build, govern, and evolve the single source of truth that powers every AI initiative in our organization.
Must-Have Experience
15+ years of hands-on data engineering and Clienthitecture 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 Clienthitectures: Databricks, Delta Lake, PySpark, Kafka, Spark Structured Streaming, cloud-native data services (AWS, Clienture).
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).

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aimlllmawsci/cd

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Effort: medium