Applied AI Engineer
First seen by Alion on Aug 21, 2026.
The Role :
You'll be embedded with the post-sales team as the engineer who turns their most painful, repetitive workflows into automations and AI applications.
This is a builder role : you'll ship internal tools, LLM-powered agents, and integrations that make onboarding, support, and account management faster and more accurate.
You'll sit close to the people using your work, iterate quickly, and measure impact in hours saved and customer outcomes.
What You'll Do :
- Build AI applications and agents that automate post-sales workflows onboarding health checks, usage/adoption reviews, ticket triage, knowledge-base answering, and SOAP/billing lookups.
- Design and ship internal automations across the post-sales stack (support, CRM, billing, data warehouse) so manual steps disappear.
- Develop LLM-powered features : retrieval over product docs and knowledge bases, summarization, drafting, and classification with an eye on accuracy and guardrails.
- Integrate systems via APIs and connectors (Slack, email, Jira, analytics/BI, internal Spry services) into reliable end-to-end workflows.
- Instrument everything : define success metrics, monitor quality, and iterate based on real usage.
- Partner directly with CS, onboarding, and support leads to find the highest-leverage problems and prototype fast.
Tech Stack & Requirements :
- 23 years of software engineering experience, with real projects shipped to production.
- Strong Python (or TypeScript/Node) and comfort building against REST APIs and webhooks.
- Hands-on experience building with LLMs prompt engineering, RAG, function/tool calling, and agent frameworks.
- A bias for shipping : you prototype quickly, get feedback, and iterate rather than over-engineering.
- Solid grasp of data - SQL, working with structured and unstructured sources, and basic pipelines.
- Clear communicator who enjoys working alongside non-engineers and translating messy processes into clean automations.
- Experience with automation platforms (Zapier, n8n, workflow orchestration) or building internal tools.
- Familiarity with customer success / support tooling, CRMs, or BI tools (Redash, Looker, etc.).
- Healthcare, RCM, or B2B SaaS domain experience.
- Comfort with cloud infra, evals/testing for LLM systems, and lightweight MLOps
Skills
Agentic AI, Artificial Intelligence, LLM, Python, Prompt Engineering, RAG, SQL, MLOps