At Appex Innovations, we believe in the power of technology to transform healthcare, we believe in being nimble and in continual innovation, we believe that every customer is unique and we believe in listening to our clients. With our deep expertise in the health care domain, solid resource base, constant industry interface and the drive to be the best in the industry, we strive to provide services that re-define health care in the years to come. The secret to our success is Appex Innovations commitment to our people and our work. We thrive on teamwork, intelligence, and innovation. Our bright and energetic employees, hailing from over different parts of the world, share a passion for leading the way to improved healthcare outcomes. Together we work diligently to add value to our clients.
Job Details: Role: Sr AI Platform Engineer Location: Bellevue, Frisco, Kansas, Atlanta (Onsite) Required experience • API development: production Python with FastAPI or comparable; versioned REST APIs, contracts, governance. • Batch and real-time data pipelines: Kafka or comparable streaming, plus CDC or incremental batch; built and operated end-to-end. • Caching and key-value serving: production Redis or Valkey; cache invalidation, TTL strategies, hot-path serving. • Vector databases and knowledge graphs: Pinecone, Weaviate, pgvector, Neo4j, or comparable; embeddings and retrieval patterns. • AI software engineering: hands-on building data infrastructure for AI and ML use cases (RAG, agent tooling, feature serving). • Azure Databricks, Delta Lake, Unity Catalog: hands-on production experience. • Delta Lake internals: transaction log, time travel, and Change Data Feed (CDF). • SQL and data modeling: comfortable with point-lookup vs analytical query patterns. • CI/CD: GitLab or GitHub Actions; automated tests for data pipelines. • Communication: works directly with senior architects, product managers, and domain stakeholders. Nice to have • Embedded analytical engines: DuckDB or comparable. • Microsoft Fabric / OneLake / Power BI Semantic Models: production experience. • SLAs and SLOs: defining and operating for data products or APIs. • MCP-style tooling: data access for AI agents. • Enterprise-scale data serving: prior work on serving infrastructure at large enterprise