AI Application Engineer

AI Application Engineer

Role Mission

Own the translation of business problems into production-ready LLM applications. This role designs the application logic around the model: prompt architecture, context management, tool calling, RAG workflows, structured outputs, evaluation loops, and user-facing behavior.

Core Responsibilities

Build AI applications such as knowledge assistants, document analyzers, email/thread analyzers, recruiting assistants, workflow copilots, and operational decision-support tools.

Design prompt systems, model instructions, retrieval flows, tool calls, conversation memory, and structured output schemas.

Integrate cloud and local LLM APIs, OpenAI-compatible endpoints, embedding models, vision models, and rerankers.

Create reusable AI workflow patterns for summarization, extraction, classification, reasoning support, report generation, and human review.

Implement guardrails for reliability, source attribution, schema validation, fallback behavior, and user confirmation before high-impact actions.

Build evaluation sets and scoring methods to measure answer quality, hallucination risk, retrieval relevance, latency, and cost.

Work with business users to turn vague automation ideas into specific AI product flows with measurable acceptance criteria.

Required Qualifications

Strong Python or TypeScript development skills.

Hands-on experience building with LLM APIs, chat completions, structured outputs, function/tool calling, or agent workflows.

Practical understanding of prompt design, context windows, token limits, retrieval augmentation, and output validation.

Ability to ship usable applications, not only prototypes or notebooks.

Comfortable debugging model behavior and separating application bugs from model limitations.

Nice-to-Have Experience

Experience with LangChain, LlamaIndex, Semantic Kernel, instructor, Pydantic, or similar frameworks.

Experience with multimodal applications involving images, OCR, audio, or document understanding.

Experience with enterprise knowledge assistants, HR tools, operations automation, customer support, or internal productivity software.

Familiarity with local LLM deployment tools such as Ollama, LM Studio, vLLM, TGI, or llama.cp

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