Jobs at CONXAI

Design and implement scalable frontend architecture for a B2B AI platform using React and TypeScript. Build reusable features, enforce domain boundaries, ensure type-safe code, write unit/integration tests, optimize performance and bundle size, integrate server-state (TanStack Query) and REST APIs, collaborate with backend and mentor junior engineers.

Lead frontend architecture and implement scalable B2B features using React and TypeScript. Design domain boundaries, build reusable components, ensure type-safe code, tests, performance optimization, and collaborate with backend and cross-functional teams to integrate APIs and AI features.

Deploy and maintain computer vision models in production: monitor failures, diagnose issues, propose algorithmic or data fixes, curate datasets, validate results for customers, and adapt SOTA architectures into efficient, impactful solutions.

Design, implement, and maintain high-performance, distributed backend services in Go. Build scalable APIs, process large multi-tenant datasets, ensure reliability via testing and observability, optimize performance, deploy on AWS using Docker/Kubernetes, and collaborate with frontend, ML, and platform teams.

Architect, deploy, and operate production-grade Kubernetes clusters and multi-region AWS infrastructure with Terraform. Build GitLab CI/CD pipelines to deploy containerized microservices via Helm/Docker, implement monitoring and autoscaling for bursty AI workloads, and ensure enterprise-grade security and compliance.

Lead quality, performance, and reliability strategy for an agentic AI SaaS platform. Architect automated testing, SLIs/SLOs, CI/CD quality gates, verify integrations with legacy AEC systems, and build a Quality Engineering team to ensure enterprise-grade uptime, latency, and throughput.

Serve as a forward-deployed technical partner translating customer workflows into scalable AI deployments. Drive adoption, own end-to-end deployments and integrations, build prototypes and demos, troubleshoot solutions, design integration architecture, and feed customer insights to product and engineering teams to scale enterprise adoption.

Lead MLOps Engineer responsible for building and automating the lifecycle of multimodal agentic AI: orchestration (LangChain/LangGraph), training pipelines for LLMs and CV, containerized model deployment, low-latency inference, model monitoring/drift detection, and optimizing ML infrastructure for performance and cost.

Design and implement an end-to-end TypeScript/Cypress test automation framework to validate non-deterministic, agentic AI outputs in AEC workflows, audit complex user journeys, ensure integrations, and automate regression and scaling tests while collaborating with Product and Engineering.

Lead enterprise customer acquisition and close high-value accounts by selling outcome-focused automation. Build ROI-driven commercial narratives, translate customer pain into product requirements, and develop account-specific land-and-expand strategies in construction and industrial sectors.

Lead development of the spatial reasoning engine for an agentic AI platform: build 3D semantic reconstruction, geometric deep learning pipelines, multi-modal fusion into spatio-temporal knowledge graphs, design agentic inference and affordance mapping, and optimize models for production at scale.

Design and deliver end-to-end computer vision and multi-agent AI architectures for AEC use cases. Lead R&D, convert SOTA research into production-ready, latency- and cost-optimized models, build multi-modal knowledge graphs to ground agents, and implement feedback loops using production telemetry to improve performance.

Design and build scalable, high-performance backend services in Go that process large-scale structured and unstructured data, orchestrate AI workflows, expose reliable APIs, ensure reliability and observability, optimize performance, deploy on AWS with Docker/Kubernetes, and collaborate with frontend, ML, and platform teams while mentoring juniors.