Sr. AI Platform Engineer

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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Certified NJ and NY Minority Business Enterprise (NMSDC)