Senior AI Platform Engineer

Employment: Full-time Experience: Mid-Senior level Education: Unspecified You'll turn AI-built prototypes into secure production systems that run on enterprise data. That means hardening Node, React, and Postgres applications, connecting them to Databricks and Snowflake, and running open-weight LLMs on dedicated GPUs. Sensitive data gets processed at scale without leaving a controlled environment or touching a public model API. This is a hands-on senior role with real ownership. You'll set the technical standards for how our applications reach production, working directly with the Senior Leadership Team and the data teams you integrate with. What you'll do Take apps built quickly with AI coding tools (Cursor, Claude Code, Lovable, and similar) and make them production-ready: architecture, authentication, testing, observability, and CI/CD. Connect Node, React, and Postgres applications securely to Databricks and Snowflake, working within their governance and access controls. Design APIs and pipelines that move data between Postgres and Databricks or Snowflake, reliably and with a full audit trail. Deploy and operate open-weight LLMs on dedicated GPU servers using vLLM or a similar inference engine. Build batch and real-time pipelines that run LLM extraction, classification, and summarization over large datasets. Keep sensitive data inside controlled environments through network isolation, encryption, secrets management, and audit logging. Nothing goes to a public model API. Monitor GPU utilization, throughput, latency, and cost, and right-size infrastructure as workloads grow. Set the standards for how AI-built prototypes reach production, and document the architecture so others can build on it. Requirements Every item below is required. 6+ years of professional software engineering, including senior-level ownership of production systems. Strong Node.js backend experience (TypeScript preferred): APIs, services, and authentication. Strong React experience building production front ends. Deep PostgreSQL experience: schema design, query tuning, and migrations. Hands-on production experience with Databricks (for example Unity Catalog, Delta Lake, Databricks SQL). Hands-on production experience with Snowflake (for example Snowpark, secure views, role-based access control). Experience deploying and serving LLMs on GPU infrastructure, including throughput and memory tuning. Solid security fundamentals for sensitive data: access control, encryption, network isolation, and secrets management. Linux, Docker, and at least one major cloud (AWS, Azure, or GCP). Comfort reviewing and refactoring AI-generated code, and using AI coding tools yourself. Professional English, written and spoken, for daily work with a US-based team. Nice to have Kubernetes, including GPU workloads Infrastructure as code (Terraform or similar) Retrieval-augmented generation (RAG) and vector search, such as pgvector, Databricks Vector Search, or Snowflake Cortex Search Databricks Mosaic AI Model Serving, Snowflake Cortex, or Snowpark Container Services Model quantization and inference optimization Large-scale document processing: OCR, entity extraction, and classification Work in regulated or data-sensitive industries such as finance, healthcare, energy, or government Databricks or Snowflake (SnowPro) certifications Benefits Work From Home Generous Salary Commensurate with Experience Flexible Working Environment