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The General Intelligence Company of New York · IT - Software Development

Applied AI Engineer - Agent

United States, New York 250000.00 - 300000.00 USD / Year · Job Posted February 21, 2026
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Job Description

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.

Job Responsibility

  • 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

Requirements

  • 4+ years backend engineering experience, preferably Python
  • 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

What we offer

  • Competitive salary and meaningful equity
  • Comprehensive benefits and flexible work setup
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