Senior Applied AI Engineer – Agent Runtime

DataSnipper

. Own base agents, including system prompt, context engineering, memory and state management for long-running tasks .

Posted 9/15/2026full-timeNew York City • New York • United StatesSenior💰 $230,000 - $290,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing agentic systems, including LLM-powered products, with a strong foundation in Python and software architecture. Proven ability to analyze and improve AI workflows while ensuring reliable delivery in regulated environments.

Highest-signal resume keywords

Production-Grade PythonLLM-Powered Product ExperienceAgentic Systems DevelopmentAI-Assisted EngineeringSoftware Architecture Fundamentals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Document ExtractionContext EngineeringMemory ManagementSystem DesignData Set BuildingExperimentationObservability StackCost OptimizationModel EvaluationTool Selection

Soft Skills

Excellent Communication Skills

Tools & Technologies

Durable Workflow EnginesRAG PipelinesVLMsOCRMCPMulti-Agent Orchestration

Industry Keywords

AuditAccountingFintechOWASP GenAI Security PracticesPrivacy-Sensitive Environments

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Own base agents, including system prompt, context engineering, memory and state management for long-running tasks
Design agent use of document extraction, retrieval, code and file sandboxes, and MCP integrations
Implement sub-agent composition, planning modes, human-in-the-loop gates, and model routing across multiple providers
Ship agent capabilities from prototype with audit domain experts through evaluation and production on Alwin
Define and build automatic processes for continuous agent improvement
Control costs and implement cost-efficient AI workflows
Analyze long agent trajectories, attribute outcomes to reasoning steps and tool calls, classify failure modes, and develop fixes
Optimize accuracy, latency and token cost
Instrument agent behavior with the observability stack and use production traces to drive improvements
Partner with Agent Experience, Document Intelligence and AI Platform teams to build self-service runtime capabilities
Evaluate new models, techniques and agent patterns and bring suitable approaches into production

Requirements

What you’ll need

5+ years of software engineering experience with strong, production-grade Python
Experience shipping and operating an LLM-powered product in production
Hands-on experience building agentic systems, including control loops, tool selection, planning versus execution, retries and fallbacks
Experience building datasets, running offline and online experiments, and using results to improve an AI system
Fluency with LLM APIs and agent frameworks across more than one model provider
Proficient with AI-assisted engineering and coding agents
Strong software architecture and system design fundamentals
Track record of reliable, well-tested delivery
Excellent communication skills
Experience with Durable workflow engines or long-running background task systems preferred
Experience with RAG and retrieval pipelines over large, messy document corpora preferred
Experience with VLMs, OCR, structured extraction and document AI metrics preferred
Experience with sandboxed code execution, MCP, or multi-agent orchestration in production preferred
Domain experience in audit, accounting or fintech preferred
Familiarity with OWASP GenAI security practices and regulated, privacy-sensitive environments preferred

Benefits

Comp & perks

Excellent salary
Stock participation plan
Flexible paid time off
Comprehensive medical and dental coverage
401K match
Paid parental leave
Company-sponsored lunch
Hybrid mode of work
Opportunities to learn and develop
International working environment
Access to OpenUp and Talkspace, the mental health and wellness platform
Multiple social activities for team building