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 behaviour.

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 behaviour, 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, structuredoutputs, 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 behaviour 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