Sr AI Platform Engineer

Appex Innovation•Frisco, TX

21h•Onsite

About The Position

We are seeking a Sr AI Platform Engineer to join our team. This role involves building and operating data infrastructure for AI and ML use cases, including RAG, agent tooling, and feature serving. You will work with technologies such as Python, FastAPI, Kafka, Redis, vector databases, Azure Databricks, and Delta Lake. The position requires strong communication skills to collaborate with senior architects, product managers, and domain stakeholders.

Requirements

Production Python with FastAPI or comparable; versioned REST APIs, contracts, governance.
Kafka or comparable streaming, plus CDC or incremental batch; built and operated end-to-end.
Production Redis or Valkey; cache invalidation, TTL strategies, hot-path serving.
Pinecone, Weaviate, pgvector, Neo4j, or comparable; embeddings and retrieval patterns.
Hands-on building data infrastructure for AI and ML use cases (RAG, agent tooling, feature serving).
Hands-on production experience with Azure Databricks, Delta Lake, Unity Catalog.
Understanding of Delta Lake internals: transaction log, time travel, and Change Data Feed (CDF).
Comfortable with SQL and data modeling, including point-lookup vs analytical query patterns.
CI/CD experience with GitLab or GitHub Actions; automated tests for data pipelines.
Strong communication skills to work directly with senior architects, product managers, and domain stakeholders.

Nice To Haves

Embedded analytical engines: DuckDB or comparable.
Production experience with Microsoft Fabric / OneLake / Power BI Semantic Models.
Defining and operating SLAs and SLOs for data products or APIs.
MCP-style tooling: data access for AI agents.
Prior work on serving infrastructure at large enterprise scale.

Responsibilities

Develop and operate production Python with FastAPI or comparable; versioned REST APIs, contracts, governance.
Build and operate end-to-end batch and real-time data pipelines using Kafka or comparable streaming, plus CDC or incremental batch.
Implement and manage production Redis or Valkey for caching and key-value serving, including cache invalidation, TTL strategies, and hot-path serving.
Work with vector databases and knowledge graphs like Pinecone, Weaviate, pgvector, Neo4j, or comparable, focusing on embeddings and retrieval patterns.
Apply AI software engineering principles to build data infrastructure for AI and ML use cases (RAG, agent tooling, feature serving).
Utilize Azure Databricks, Delta Lake, and Unity Catalog in a production environment.
Leverage understanding of Delta Lake internals, including transaction log, time travel, and Change Data Feed (CDF).
Apply SQL and data modeling skills, comfortable with both point-lookup and analytical query patterns.
Implement CI/CD pipelines using GitLab or GitHub Actions, including automated tests for data pipelines.
Communicate effectively with senior architects, product managers, and domain stakeholders.