Role: Sr. AI Platform Engineer
Location: Bellevue, WA / Frisco, TX / Kansas City, KS / Atlanta, GA
Duration: 12+ Months
Responsibilities:
- Build serving stores, sync pipelines, and API layers for pilot use cases.
- Configure each pilot end-to-end: source table binding, key schema, sync schedule, and consumer integration.
- Set up CI/CD pipelines with automated tests covering sync correctness, API contract validation, and latency benchmarks.
- Operate to defined SLAs for latency, freshness, and availability.
- Partner with domain teams (Consumer & Marketing, Commercial & Revenue, Growth, Pricing & Analytics) to onboard their use cases onto the patterns we build.
- Contribute to reference implementations, blueprints, and documentation that future teams will reuse.
Requirements:
- 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
Best Regards Gunika Sharma
Technical Recruiter Empower Professionals Inc
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