Senior Software Engineer – AI Platform

Accela•Birmingham, AL

13h•$170,000 - $210,000•Remote

About The Position

We are seeking a Senior Software Engineer – AI Platform to build and evolve Accela's AI-powered Document Compliance Platform. In this role, you will design, develop, and optimize AI-driven document verification services that leverage Vision-Language Models (VLMs) to analyze building permit documents against regulatory requirements and jurisdiction-specific codes. You will work across the AI application stack—including prompt engineering, model evaluation, backend services, Azure-native cloud infrastructure, and frontend application development using React and Next.js—to deliver scalable, production-ready AI capabilities. Working closely with Product Management, Engineering, AI, and Construction Subject Matter Experts (SMEs), you will improve model accuracy, optimize performance and cost, and deliver intelligent document compliance solutions for permitting agencies.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Artificial Intelligence, or a related technical discipline, or equivalent practical experience.
Strong experience designing prompts and workflows for Vision-Language Models (VLMs) and Large Language Models (LLMs), including GPT-family and Gemini models or equivalent.
Demonstrated experience designing AI evaluation frameworks, including datasets, accuracy metrics, precision, recall, F1 scoring, and per-example analysis.
Strong understanding of multimodal AI systems that combine document images with natural language prompts.
Strong experience with Python, FastAPI, Pydantic, asynchronous programming, and API development.
Experience optimizing AI systems for inference cost, latency, scalability, reliability, and operational performance.
Strong analytical, troubleshooting, and problem-solving skills.
Excellent written and verbal communication skills, with the ability to collaborate effectively across cross-functional teams.

Nice To Haves

Experience with Azure OpenAI Service, Google Vertex/Gemini, or comparable enterprise AI platforms.
Experience building AI-powered document processing, document intelligence, or intelligent automation solutions.
Experience with Retrieval-Augmented Generation (RAG), embeddings, vector databases, semantic search, or agentic AI workflows.
Experience deploying and operating AI applications using Azure cloud services, containerization technologies (Docker/Kubernetes), and CI/CD pipelines.
Experience developing modern web applications or data-intensive user interfaces using React, Next.js, or similar frameworks.
Background in Architecture, Engineering, Construction (AEC), permitting, or regulatory compliance software.

Responsibilities

Design, develop, and optimize prompts for Vision-Language Models (VLMs) that support document classification, text extraction, and code compliance validation.
Design, build, and maintain automated AI evaluation pipelines that measure model accuracy, latency, precision, recall, F1 score, cost, and other quality metrics.
Evaluate and benchmark Azure OpenAI, Google Gemini and other foundation models, recommending improvements based on quality, performance, and operational cost.
Implement structured output extraction using function calling, JSON mode, Pydantic models, and schema validation techniques.
Design, develop, and maintain Python-based AI services using FastAPI and asynchronous programming patterns.
Partner with Product Management, Construction SMEs, and Engineering leadership to define technical strategy and translate complex regulatory and business requirements into scalable AI platform capabilities.
Monitor, troubleshoot, and resolve production issues across AI inference services, application layers, and cloud infrastructure.
Optimize AI workflows for scalability, reliability, latency, and operational efficiency while managing inference costs.
Contribute to architectural decisions involving AI services, cloud infrastructure, model orchestration, and application integration.
Mentor engineers and contribute to engineering best practices, code quality, technical design reviews, and knowledge sharing.
Lead the technical design and architecture of AI platform capabilities, ensuring scalability, maintainability, and long-term extensibility.
Drive technical direction through architecture reviews, engineering standards, and cross-functional collaboration on complex AI initiatives.