AI Engineer (LLM & Agentic AI)

This position is for an Applied AI Engineer at Aiden Risk, focused on building and deploying production-ready AI systems. The ideal candidate will have experience transforming large language model (LLM) capabilities into reliable and scalable applications. These applications may include intelligent agents, retrieval systems, browser automation, and data extraction tools. This role requires hands-on engineering skills to develop solutions that perform well under real-world conditions.

Key Responsibilities
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines and vector search systems tailored to real-world document datasets.
- Develop AI agents capable of tool calling, integrating with external APIs, and managing automated workflows.
- Implement browser automation and web data extraction using Playwright.
- Integrate LLMs from providers such as OpenAI, Anthropic, Gemini, and open-source models into production applications.
- Design and develop evaluation frameworks to measure AI system accuracy, reliability, latency, and operational cost.
- Build, test, and deploy Python-based AI services using Docker containers and cloud platforms.
- Continuously improve system performance, troubleshoot issues, and ensure reliability under production workloads.

Required Qualifications
- 1 to 2 years of software engineering experience with strong proficiency in Python programming.
- Practical experience building applications using LLMs or generative AI models.
- Solid understanding of RAG, embeddings, vector search, and familiarity with vector databases such as pgvector, Pinecone, or Chroma.
- Experience with AI frameworks like LangChain or similar tools.
- Basic knowledge of APIs, Docker, and cloud platforms such as AWS or Google Cloud Platform (GCP).
- Strong problem-solving skills and an engineering mindset focused on delivering tested, deployable solutions.

Preferred Qualifications and Benefits
- Experience with browser automation tools like Playwright is considered a plus.
- Candidates who have built practical AI applications beyond tutorials or demos and understand how to ensure their reliability in production environments will be highly valued.

This role is not suitable for candidates looking for entry-level or purely experimental AI projects. Instead, it demands an engineer who can deliver robust, scalable AI-powered applications that operate effectively in real-world scenarios.