AI Engineer

About the Role

SelfInspection is transforming vehicle inspections through AI-powered computer vision and intelligent automation.

We are looking for a Senior AI Engineer to design, build, and deploy AI-powered products and systems that enhance inspection workflows, automate decision-making, and improve customer experiences.

You will work on cutting-edge applications of Generative AI, Large Language Models (LLMs), Agents, Retrieval-Augmented Generation (RAG), and Multimodal AI. You will collaborate closely with Computer Vision Engineers, Backend Engineers, Product Managers, and Leadership to deliver AI solutions that create measurable business value.

This is a highly hands-on role requiring strong software engineering skills and deep experience building AI systems in production.

Responsibilities

AI Product Development

• Design and develop AI-powered features and products
• Build intelligent workflows using LLMs, agents, and automation frameworks
• Develop systems that combine structured data, documents, images, and AI models
• Evaluate and integrate state-of-the-art AI technologies into the platform

Generative AI & LLMs

• Build applications using Large Language Models (OpenAI, Anthropic, Gemini, Open Source Models)
• Design and optimize prompts, system instructions, and tool usage
• Develop Retrieval-Augmented Generation (RAG) systems
• Implement AI workflows for information extraction, summarization, reasoning, and decision support

Agentic Systems

• Design and implement AI agents capable of performing multi-step tasks
• Build agent orchestration systems and tool integrations
• Develop workflows involving planning, reasoning, memory, and execution
• Evaluate agent performance and reliability

AI Infrastructure

• Build scalable AI services and APIs
• Develop evaluation frameworks for AI systems
• Implement monitoring, observability, and performance tracking
• Optimize AI costs, latency, and reliability

Multimodal AI

• Work with systems combining:
  • Text
  • Images
  • Documents
  • Structured Data
  • Computer Vision Outputs
• Develop AI solutions that leverage vehicle inspection data and reports

Collaboration

• Work closely with Product and Engineering teams to identify AI opportunities
• Translate business requirements into production-ready AI systems
• Communicate technical tradeoffs and implementation strategies
• Mentor other engineers and contribute to AI best practices

Required Qualifications

• 5+ years of software engineering experience
• 3+ years building AI/ML-powered applications
• Proven experience deploying AI systems to production
• Strong backend engineering skills
• Experience building customer-facing AI products
• Strong understanding of AI system design and architecture

Technical Skills

Generative AI

• OpenAI APIs
• Anthropic APIs
• Gemini APIs
• Open Source LLMs (Llama, Mistral, Qwen, DeepSeek)

Agent Frameworks

Experience with one or more:

• LangGraph
• LangChain
• CrewAI
• AutoGen
• OpenAI Agents SDK
• MCP (Model Context Protocol)

Retrieval & Search

• Vector Databases
• RAG Architectures
• Embeddings
• Semantic Search

Experience with tools such as:

• Pinecone
• Weaviate
• Qdrant
• pgvector

Programming

• Python (required)
• TypeScript (preferred)
• SQL

Backend Engineering

• REST APIs
• Microservices
• Event-Driven Architectures
• Distributed Systems

Cloud & Infrastructure

• AWS (preferred)
• Docker
• Kubernetes
• CI/CD

Preferred Qualifications

• Experience building AI products from concept to production
• Experience with multimodal AI systems
• Experience combining Computer Vision and LLM-based workflows
• Experience fine-tuning open-source models
• Experience with evaluation frameworks and AI testing methodologies
• Experience building AI copilots, assistants, or agentic systems
• Startup experience and ability to work in fast-moving environments

Nice to Have

• Experience in automotive technology
• Experience in insurance technology
• Experience with Computer Vision systems
• Experience with OCR and document understanding
• Experience with knowledge graphs
• Experience with AI safety, guardrails, and governance

What Success Looks Like

• AI-powered features that improve customer experience and operational efficiency
• Reliable and scalable AI services running in production
• Reduced manual effort through intelligent automation
• Strong evaluation and monitoring practices for AI systems
• Effective collaboration with Computer Vision and Engineering teams
• Rapid experimentation and delivery of new AI capabilities