Applied AI Engineer - Agent
The General Intelligence Company of New York generalintelligencecompany.com Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at The General Intelligence Company of New York. New York · full-time · $250,000–$300,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Improve the agent’s planning, tool use, memory, safety checks and recovery behavior from design through release
- Build tests and evaluation pipelines that track task success, latency, cost and failure patterns across agent versions
- Bring LLM methods such as retrieval, tool calling and multi-agent handoffs into production, while reducing hallucinations
- Strengthen backend orchestration, data flows and observability so agent actions remain reliable and traceable
- Work with product and infrastructure teams to set success measures, test changes and deploy improvements
What you bring
- At least four years in backend engineering, with Python preferred and demonstrated impact valued over tenure
- You have taken LLM features into production using prompting, tool calls, retrieval, embeddings and evaluation methods
- You have built evaluation systems and used regression tests, task outcomes and cost or runtime measures to improve performance
- You understand distributed systems, including concurrency, reliability, performance, data modeling and lifecycle management
Who this fits
This hands-on role suits a mid-level engineer who wants ownership across prototyping, evaluation and production releases. You’ll work onsite in New York. The stated salary range is $250,000 to $300,000 per year.
From the employer
We’re hiring an Applied AI Engineer to push the boundaries of our Cofounder agent. You’ll own core backend systems and applied LLM work: advancing agent reliability and autonomy, building evaluation pipelines, and shipping techniques that measurably improve agent performance. This is a hands-on role with high ownership across research-to-production: prototyping, instrumenting, evaluating, and deploying improvements that show up directly in user outcomes.
What You’ll Do
Design and implement agent improvements end-to-end: prompting strategies, tool selection, action planning, memory usage, safety/guardrails, and recovery paths
Build robust evaluation pipelines for the agent: offline evals (golden tasks, regression suites, behavior tests), online metrics (latency, success rate, fallout modes, cost efficiency), and experimentation frameworks (A/B, canaries, guardrail thresholds)
Productionize applied LLM techniques: function/tool-calling orchestration, self-reflection, retrieval/RAG, multi-agent handoffs, caching/embedding strategies, and hallucination reduction
Improve core backend systems: reliable job orchestration, retries/backoff, idempotency, and auditability; scalable memory and context routing; data pipelines across Gmail, Slack, Notion, Linear, Google Workspace, etc.; observability and tracing for agent actions/outcomes
Partner with product and infra to define success metrics and ship fast, safe iterations
Write clean, well-tested code; document design decisions and runbooks
What You’ll Bring
4+ years backend engineering experience, preferably Python (we care about impact over years)
Hands-on LLM experience: prompt engineering, function-calling, retrieval, embeddings, evaluation design; you’ve shipped LLM features to production
Track record building evaluation harnesses and using them to drive improvements (regression suites, task success metrics, cost/runtime tradeoffs)
Solid distributed systems fundamentals: concurrency, reliability, performance, data modeling, lifecycle management
Pragmatic experimentation: hypothesis → prototype → measured improvement → rollout
Excellent debugging and instrumentation skills; you enjoy finding and fixing edge cases in the wild
Nice To Have
Experience with agent frameworks, tool orchestration, and memory architectures
RAG systems in production (chunking, retrieval quality, freshness strategies)
Redis, Postgres/Supabase, queues (e.g., Celery/Arq/SQS), and event-driven designs
Observability stacks (Datadog, OpenTelemetry), and cost/latency optimization
Why Join Us
Mission: build autonomous agents that run entire businesses
Impact: ship core agent improvements that users feel immediately
Velocity: small, senior team; fast decision cycles; high ownership
Stack: modern tooling across AI orchestration, integrations, and memory systems
Compensation
Competitive salary and meaningful equity
Comprehensive benefits and flexible work setup