Top Tech Jobs & Startup Jobs

Build production-ready intelligent automations and agentic AI solutions. Full-stack development using TypeScript or Python, LangChain, vector DBs, RAG, and tool-calling. Collaborate with clients, ensure maintainable, tested, scalable deployments, CI/CD, observability, and model fine-tuning or deployment.

Build and operate durable, distributed enterprise systems: implement Java services, Temporal workflows, and Kafka pipelines; design and run Kubernetes-native infrastructure on AWS; lead migrations and platform modernization; consult with clients, ensure CI/CD/GitOps delivery, and enable observability and reliability for application teams.

Owner of Avalara's AI workflow platform (starting with n8n): design, deploy, and operate secure, multi‑environment infrastructure with IaC and CI/CD; embed governance, observability, SLOs, and AI‑enhanced operations; create reusable patterns and mentor teams to raise DevOps and AI maturity.

The Full-Stack Applied AI Engineer will design and build AI-enabled products, own delivery from UI to backend, and implement robust systems with a focus on security and reliability, collaborating closely with clients and team members.

As the Lead AI Architect, you'll design and build a workflow automation platform, deploy solutions with AI, and lead client engagements autonomously.

Lead design, deployment, and operation of a secure, enterprise AI workflow platform (starting with n8n). Own infra, CI/CD, IaC, Kubernetes, observability, security, and governance. Define SLOs, reusable patterns, and guardrails; mentor teams and embed AI into operations to improve reliability, automation delivery, and platform maturity.

Lead design and engineering of enterprise AI-powered automation using n8n as an orchestration platform. Build scalable, secure API-first and event-driven integrations, AI-enabled workflows and agents, CI/CD and governance, observability, and reusable automation frameworks. Provide technical leadership, mentor engineers, and drive platform standards and operational excellence.

Define and drive enterprise AI automation architecture and governance for scalable, secure, API-first and event-driven workflows. Create reusable automation patterns, influence cross-functional roadmaps, mentor engineers, and ensure measurable operational and business outcomes.

Translate complex business workflows into implementation-ready requirements for AI-enabled automation and agentic workflows. Map current/future-state processes, define APIs, triggers, data mappings, acceptance criteria, and governance. Collaborate with AI Automation Engineers, architects, and stakeholders to ensure scalable, auditable, and measurable AI automation solutions and support delivery, testing, and continuous improvement.