AI Engineer

What you'll do

• Architect, build, and scale AI-powered products and automations for commercial organization
• Identify high-value use cases and build intelligent workflows and agentic applications
• Deploy trustworthy AI systems across Salesforce, Snowflake, Slack, and other internal platforms
• Design, build, deploy, and maintain AI-powered automations and agent workflows using modern orchestration frameworks
• Translate business pain points into modular, extensible automation flows that are observable, debuggable, and fault-tolerant
• Build production-grade LLM applications for knowledge surfacing, workflow routing, decision support, and dynamic content generation
• Maintain governance model for prompt engineering standards, audit trails, and feedback loops
• Work cross-functionally to discover automation opportunities, prototype quickly, document tooling, and drive self-service adoption
• Own and drive the AI Ops roadmap prioritized by business impact and feasibility
• Establish internal thought leadership on applied AI

Requirements

• 5–8+ years in systems automation, internal tools, or process/data engineering
• Hands-on experience with orchestration platforms such as n8n, LangGraph, Zapier, or Make
• Strong familiarity with SaaS APIs and system interoperability
• Experience building production-grade LLM applications using ChatGPT Enterprise and related LLM APIs
• Maintain governance model for prompt engineering, agent testing, and audit trails
• Familiarity with evaluation and observability frameworks for LLM applications
• Ability to work cross-functionally with Sales, Client Services, Media Strategy, Marketing, Product, and Ops
• Own and communicate AI Ops roadmap
• Nice to have: prompt libraries, embeddings-based retrieval, vector databases (Pinecone, Weaviate), RAG pipelines
• Nice to have: Retool or Streamlit for lightweight internal UIs
• Nice to have: ArgoCD or Kubernetes CI/CD experience

Tech stack