AI Engineer

Key Responsibilities

• Design and build AI-powered features using LLMs (GPT-4, Claude, Gemini, open-source models) through prompt engineering, fine-tuning, and RAG architectures
• Develop and maintain RAG pipelines including document ingestion, chunking strategies, embedding generation, vector storage, and retrieval optimization
• Build evaluation frameworks to measure AI feature quality including accuracy, latency, cost, and user satisfaction metrics
• Implement guardrails, content filtering, and safety mechanisms to ensure AI outputs are reliable and appropriate
• Integrate AI capabilities into existing product features through well-designed APIs and SDKs
• Optimize AI system costs by selecting appropriate models, caching strategies, and prompt optimization techniques
• Stay current with rapidly evolving AI landscape and evaluate new models, tools, and techniques for product applicability

Required Skills & Experience

• 3+ years of software engineering experience with at least 1 year building LLM-powered applications
• Strong Python programming skills and experience building production APIs (FastAPI, Flask, or similar)
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Google) and LLM orchestration frameworks (LangChain, LlamaIndex, or custom)
• Understanding of RAG architectures including vector databases (Pinecone, Weaviate, Qdrant, pgvector), embedding models, and retrieval strategies
• Experience with prompt engineering techniques: few-shot learning, chain-of-thought, structured outputs, and system prompting
• Knowledge of AI evaluation methodologies and the ability to build systematic evaluation pipelines
• Solid software engineering fundamentals: version control, testing, CI/CD, and production deployment

Nice-to-Have

• Experience fine-tuning open-source LLMs (Llama, Mistral) using techniques like LoRA or QLoRA
• Familiarity with AI agent frameworks and multi-step reasoning systems
• Experience with multimodal AI (vision, audio, or video models)
• Knowledge of model quantization and optimization for edge or cost-constrained deployment
• Background in traditional ML or NLP is valuable but not required

Tech Stack

What We Offer

• Competitive salary and equity at [Company Name]
• Access to latest AI models and generous API budgets for experimentation
• Fast-moving team at the forefront of applied AI
• Comprehensive health, dental, and vision insurance
• Flexible remote-first work with optional in-person collaboration
• Opportunity to shape the AI strategy and product direction at [Company Name]

Interview Process

1. 1Recruiter phone screen (30 min) — background, AI experience, and role expectations
2. 2Technical screen (60 min) — Python coding, LLM concepts, and a prompt engineering exercise
3. 3System design interview (60 min) — design a RAG-based feature or AI agent for a realistic product scenario
4. 4Take-home or live coding (2-3 hours) — build a working AI feature using LLM APIs, demonstrate evaluation approach
5. 5Team and hiring manager conversation (60 min) — collaboration style, AI philosophy, and career goals
6. 6Reference checks and offer