Hiver

Role Description The Forward Deployed AI Engineer is the person who closes that gap. You embed (virtually) with strategic customer accounts, understand how their support and operations teams actually work, and then build — production-grade configurations, automations, knowledge pipelines, and integrations that make Hiver’s AI deliver measurable outcomes in their environment. You stay until it works, and you carry what you learn back into the product. This is an engineering role, not a sales role. No quota, no commission, no demo circuit. Your success metric is whether the customer’s AI deployment is live, trusted, and adopted. Key Responsibilities - Embed with strategic accounts. Join shared Slack channels, sit in on the customer’s team rituals, shadow real ticket queues, and map how work actually flows through their shared inboxes — all remotely. - Build the last mile. Design and ship customer-specific AI configurations: Playbook automations, KB ingestion and chunking strategies, triage and tagging taxonomies, custom integrations against the customer’s stack via APIs and webhooks. - Own deployments end-to-end. From discovery through go-live through stabilisation. - Diagnose and fix automation issues, explaining them in understandable terms to the customer’s ops lead. - Make AI trustworthy account-by-account. Build per-account golden datasets, run evals against the customer’s real traffic patterns, and gate rollouts on measured quality. - Be the product’s field intelligence. Every gap you hand-build is a roadmap signal. - Work closely with the AI product and engineering teams to turn repeated custom work into product capabilities. - Drive adoption, not just go-live. Partner with CSMs on activation: train champion users, instrument usage, and iterate until the customer’s team reaches for the AI features by default. Qualifications - 4–6 years as a software engineer, with at least 1–2 years working hands-on with LLM-powered systems in production: prompt and context engineering, RAG pipelines, agentic workflows, eval harnesses. - Strong Python; comfortable with TypeScript/JavaScript for full-stack work (dashboards, integrations, internal tools). - Real API/integration experience — you’ve connected messy third-party systems before and know that the documented behaviour and the actual behaviour are different things. - Excellent written and verbal communication skills. - High ownership and comfort with ambiguity. - Comfortable with the 3 PM – 12 AM IST schedule. Nice to Have - Experience at a B2B SaaS company in a solutions, implementation, or platform engineering capacity. - Familiarity with customer support / CX tooling (helpdesks, shared inboxes, ticketing systems). - Exposure to LLM observability and eval tooling (Langfuse, LLM-as-judge patterns, golden datasets). - Prior experience as a founder, early-stage employee, or consultant. What This Role Is Not - Not a sales engineer role. You enter after the deal (or late in it, for technical validation on strategic accounts). You don’t carry a quota. - Not a support escalation role. You build; you don’t run a ticket queue. - Not a travel role. Embedding happens through the customer’s Slack, their Hiver workspace, and recurring working sessions. Why This Role Matters Hiver is moving from AI features to AI outcomes. The companies winning in AI-first SaaS — from Palantir to OpenAI to Databricks — learnt the same lesson: the hardest part of AI isn’t the model, it’s making it work inside a real organisation's messy reality. This role is how we do that for our most important customers, and how what we learn there shapes the product for everyone else.