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