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Remote role plus visible senior AI title, but niche specialized skillset reduces applicant density.
Role demands combined AI, data-engineering and full-stack consulting experience, making cross-industry transfer difficult.
Many mandatory technical depth requirements across data, backend, frontend, and AI integration increase filter rigidity.
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Own end-to-end delivery of AI-powered data products from discovery, architecture, and design to production deployment and client delivery.
Design and build robust data foundations including data models, schema, ETL/ELT pipelines, and implement AI capabilities such as RAG pipelines and agentic workflows.
Develop full-stack applications using Python/FastAPI for backend and Next.js for frontend, orchestrate automation pipelines, and manage CI/CD and cloud deployments on AWS or Azure.
Strong production experience in data engineering encompassing data modeling, dimensional modeling, ETL/ELT pipelines, and dbt-based modular SQL transformations.
Proficient in full-stack development with Python (including FastAPI), Next.js, PostgreSQL, and experience with modern data platforms like Snowflake or Databricks.
Experience owning CI/CD pipelines and deploying on AWS or Azure without heavy support.
Work Experience Required: Not explicitly mentioned in the JD.
Comfortable blending hands-on engineering with client-facing solution shaping, able to translate ambiguous requirements into concrete system designs.
Operates independently with strong end-to-end ownership from architecting data models to delivering full-stack AI applications in production.
Demonstrates advanced AI-paired engineering skills using agentic coding tools (e.g., Claude Code) as a primary development accelerator and can communicate technical trade-offs to both technical and business audiences.