Job Description

Data Engineer- AI & PlatformLocation: US - REMOTE (EST/CST)Duration: Direct HireSalary Range: $140,000 - $160,000 per annumBenefits: This is a direct hire opportunity. The selected candidate will be employed directly by our client. All compensation and benefits, including but not limited to medical insurance, retirement plans, paid time off, and other perks, will be provided by the client in accordance with their internal policies and subject to applicable laws and eligibility requirements.Responsibilities:Build and Scale AI-Ready Data InfrastructureDesign, build, and maintain scalable and self-healing data pipelines that ingest, transform, and serve data from dozens of source systems (PMS, CRM, financial systems, IoT, web/mobile analytics, and third-party providers).Build and maintain MCP (Model Context Protocol) server integrations that expose data to LLM-powered tools and AI agents across the organizationQualification:5+ years of professional data engineering experience building and operating production data platforms.Deep expertise with Databricks, Spark, or similar distributed data processing frameworks.Strong SQL skills and data modeling experience across analytical (star schema, data vault) and AI workloads, with a firm grasp of keys, grain, referential integrity, data quality, and what it takes to certify a "gold" data asset.Deep experience with AI coding toolsProficiency in Python; experience with orchestration tools (Airflow, Dagster, or Databricks Workflows).Experience with cloud data platforms and relational back ends such as Postgres.Experience building data infrastructure that supports Machine Learning workflows: feature stores, training pipelines, embedding generation, and model serving.Familiarity with LLM integration patterns including RAG architectures, vector databases (Pinecone, Weaviate, or similar), and MCP or tool-use frameworks.Understanding of how AI models consume data and the engineering requirements for reliable, low-latency AI data serving.Awareness of AI governance considerations: data provenance, bias detection, and responsible AI data practices.Tools & Technologies:Databricks, Spark, Delta Lake, Unity Catalog (domains, metric views).Python, SQL, dbt or similar transformation frameworks.Postgres and other relational back ends.Azure cloud services (ADLS, Azure, Synapse) or equivalent; exposure to Azure Web Apps and API layers a plus.Git, CI/CD, infrastructure as code (Terraform or similar).Data catalog, lineage, and observability tools (Monte Carlo, Great Expectations, or similar).MCP, RAG frameworks, and LLM-powered analytics a plus.

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