AI Developer - LLM Features & AI Systems

STAN AI

$90K — $130K *
Enterprise Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of hands-on experience with building RAG systems in production environments
  • Proficient in embedding models and vector databases such as Pinecone or Weaviate
  • Demonstrated experience with multi-step reasoning systems and agent loops
  • Familiarity with Claude and OpenAI APIs, including prompt design and function calling
  • Strong programming skills in TypeScript and Python, emphasizing clean and maintainable code
  • Knowledge of LLM limitations and the tradeoffs involved in AI deployments

Responsibilities

  • Design and build retrieval-augmented generation (RAG) systems, including optimization strategies
  • Implement and manage vector search infrastructure, integrating with core data layers
  • Build multi-step agent loops that address edge cases like hallucination and reasoning failures
  • Integrate APIs from Claude and OpenAI using orchestration frameworks
  • Establish evaluation pipelines to assess LLM output quality and iterate based on data
  • Collaborate with product teams to scope and advise on AI feature feasibility
  • Utilize AI tools to enhance team productivity and optimize the development process

Benefits

  • Comprehensive health, dental, and specialist benefits
  • Company-provided Macbook
  • Free parking and shuttle service to the office
  • Extra PTO for occasional US holidays
  • Company events, in-office restaurant, and building-wide perks
  • Unlimited ping pong and espresso
Full Job Description
We're looking for an AI Developer to build the AI backbone of our product - retrieval-augmented generation pipelines, multi-step agent workflows, embedding systems, and LLM integrations that property managers rely on daily.

You'll work directly with product and engineering to ship AI features end-to-end: designing vector search strategies, building agent loops, evaluating model quality, and shipping systems that actually work in production. You won't just execute tickets - you'll bring a point of view on embedding models, chunking strategies, reranking approaches, and the real tradeoffs between quality, latency, and cost.

Tasks

  • RAG Pipelines: Design and build retrieval-augmented generation systems. Own chunking strategy, embedding selection, retrieval optimization, and reranking.
  • Vector Databases: Implement and manage vector search infrastructure (Pinecone, Weaviate, or similar). Integrate embeddings with our MongoDB core data layer.
  • Agent Workflows: Build multi-step agent loops with tool use, memory, planning, and guardrails. Handle edge cases like hallucination, context limits, and reasoning failures.
  • LLM Integration: Integrate Claude and OpenAI APIs using orchestration frameworks (LangChain, LlamaIndex, or equivalent). Manage prompts, context windows, streaming, function calling, and tool use.
  • Evals & Quality: Build evaluation pipelines to measure LLM output quality. Iterate on prompts, retrieval strategies, and model choices based on real data.
  • AI Tooling & Developer Experience: Use Claude Code and modern AI-assisted development as part of your workflow. Help the team ship faster with AI tools.Collaboration & Architecture: Work with product to scope AI features and advise on feasibility. Help set patterns and best practices as the AI feature set grows.


Requirements

Must Have:

  • Hands-on experience building RAG systems in production (chunking, embedding, retrieval, reranking)
  • Real experience with embedding models (OpenAI, Cohere, or open-source) and vector databases (Pinecone, Weaviate, Chroma, or similar)
  • Experience building agent loops or multi-step reasoning systems (tool use, memory patterns, error handling)
  • Familiarity with Claude API and/or OpenAI API - prompt design, function calling, streaming
  • Strong TypeScript and Python - you write clean, maintainable, well-tested code
  • Understanding of LLM limitations: hallucination, context windows, latency, inference cost, and real-world tradeoffs

Strong Assets:

  • Experience with Claude Code or AI-assisted development workflows
  • Knowledge of LLM evaluation frameworks (RAGAS, custom metrics, semantic similarity scoring)
  • Side projects or portfolio demonstrating real AI work (not tutorials) - GitHub, demos, case studies
  • Hands-on experience with orchestration frameworks (LangChain, LlamaIndex, or equivalent)
  • Experience with multi-modal inputs or structured output extraction (JSON mode, schema validation)
  • Background shipping AI features in a production SaaS environment (not just experiments)
  • Familiarity with Stan AI stack: Node.js, TypeScript, MongoDB, AWS
  • Understanding of prompt engineering, few-shot learning, and in-context optimization

Nice to Have:

  • Fine-tuning or RLHF experience
  • Contributions to open-source AI projects
  • Experience in PropTech, FinTech, or operations software
  • Knowledge of prompt injection risks and AI safety patterns
  • Familiarity with vector database administration (indexing, cost optimization, scaling)


Benefits

  • Competitive salary
  • Comprehensive health, dental, and specialist benefits.
  • Company Macbook.
  • Free parking and shuttle service to the office.
  • Extra PTO during occasional US holidays.
  • Company events, in-office restaurant, and building-wide perks.
  • Unlimited ping pong and espresso!


Most "AI developer" roles mean adding a ChatGPT call to an existing feature. This is different. You'll be building the AI backbone of a product that property managers depend on daily to run their business. You'll make real architectural decisions: embedding models, retrieval strategies, chunking approaches, evaluation metrics. You'll see the results ship and hear directly from customers.

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