Applied AI Engineer (LLM Systems & Integration)

Responsibilities

• Design, develop, and optimize production-grade LLM applications and RAG pipelines using vector, keyword, and hybrid retrieval techniques.
• Develop controlled agentic AI workflows with tool integration, defined autonomy levels, Human-in-the-Loop (HITL) approval gates, authorization controls, and exception handling.
• Build secure and governed AI data-access and context-engineering pipelines, ensuring appropriate data permissions, ownership, metadata usage, and efficient context selection.
• Integrate AI services with enterprise platforms and API gateways, ensuring schema validation and clear separation between probabilistic AI outputs and deterministic business logic.
• Implement reliable integration patterns, including retries, timeouts, error handling, idempotency, and health monitoring.
• Establish AI evaluation and quality frameworks to measure accuracy, relevance, hallucinations, structured-output reliability, and regression performance.
• Benchmark and optimize model and inference performance, including latency, TTFT, throughput, concurrency, and context-length requirements.
• Implement comprehensive AI observability, traceability, and version control across models, prompts, context, tool calls, and execution logs.
• Ensure AI solutions comply with information security, privacy, data-retention, and operational-control requirements, while continuously monitoring quality after deployment.

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