Senior AI Integration Engineer
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Job Description
Senior Integration Engineer responsible for building secure APIs and data delivery services for AI applications. Requires extensive experience in backend engineering and high-performance systems.
Responsibilities:
- Build high-performance, secure, and production-grade API layers
- Develop custom Model Context Protocol (MCP) servers
- Implement real-time data delivery services optimized for human applications and autonomous AI agents
- Operate at high throughput — handling thousands of requests per second
- Build integration layers relied on by AI agents
Requirements:
- **Must-Have**
- Overall Experience: 8+ years in Backend Software Engineering, Distributed Systems Architecture, or Enterprise Platform Engineering
- Resiliency Engineering: 4+ years architecting mission-critical systems requiring "four-nines" (99.99%) availability, rigorous traffic shaping, and self-healing systems
- AI & Agentic Systems: 2+ years building production-grade integration layers, semantic context gateways, or RAG endpoints consumed by LLMs and autonomous AI agents
- Proficiency in Advanced API Design & Context Optimization
- Production-level proficiency in one or more of: C# (.NET Core), Java, Python, or Node.js/TypeScript
- Proficiency in Enterprise Scalability & Traffic Management
- Proficiency in Lock & Contention Mitigation (API & Database Tier)
- Proficiency in Hardened Security, Governance & AI Isolation
- **Preferred Experience**
- GraphQL and dynamic JSON/gRPC filtering layers (zero-waste data fetching)
- Custom MCP server development for LLM context exposure
- Heterogeneous schema merging for LLM tool-calling (e.g., relational DBs, analytics platforms like Pendo/Hotjar, observability tools)
- Semantic token-aware API pagination and streaming
- Context-aware gateways for conditional object graph hydration based on agent intent
- Edge performance: CloudFront Functions, Lambda@Edge, HTTP/3, WebSockets
- PostgreSQL and OpenSearch/Elasticsearch access layers with sub-millisecond caching
- Reactive streams and rate-limiting for Amazon MSK (Kafka), SQS, and HTTP endpoints
- Circuit Breakers, Bulkheads, and Retry-with-Exponential-Backoff (Polly, Resilience4j, or equivalent)
- Amazon MemoryDB / Redis OSS / Valkey for rate limiters, session caches, and semantic query caching
- Optimistic Concurrency Control (OCC) via eTags/versions; Pessimistic Locking only where strictly necessary
- CQRS separating high-volume writes from intensive read queries in PostgreSQL
- Asynchronous write delegation for high-contention API writes to Kafka/MSK
- Zero Trust security: OAuth2, OIDC, mTLS, and AWS Lake Formation
- Security proxy layers over Amazon Bedrock (prompt injection filtering, PII redaction, API token quota enforcement)
- End-to-end API runtime tracing with OpenTelemetry (oTel) and Datadog
Benefits:
- Remote work
- 13 floating holiday
- 15 vacation days per year completed
- Good working environment
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