Member of Technical Staff – Applied Machine Learning (AI Agents | LLMs | Production AI)

Margaretville, NY, US Senior AI Agent Developer

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Skills & Technologies

Prompt EngineeringPythonRag

About This Role

AI job market dashboard showing open roles by category

Hiring \| Member of Technical Staff – Applied Machine Learning (AI Agents \| LLMs \| Production AI)

New York City (On\-site 5Days/week)

$175K\+Base with Competitive Equity

Visa Sponsorship Available

Full\-Time

The next wave of AI won't be defined by better chatbots—it will be defined by autonomous AI systems that perform real work.

We're hiring Applied Machine Learning Engineers who want to build production\-grade AI agents capable of solving complex accounting and finance problems at scale.

This is not a research role.

This is an opportunity to take cutting\-edge AI research and transform it into reliable, scalable products used by real customers every day. You'll build intelligent systems that reason, plan, retrieve information, evaluate themselves, and continuously improve—all while operating in production environments where performance, reliability, latency, and accuracy matter.

Why This Role Is Different

Many ML roles focus on model training or publishing research.

This role is about shipping AI systems that solve real business problems.

You'll own problems end\-to\-end—from identifying opportunities and designing system architecture to experimentation, deployment, evaluation, and continuous optimization.

You'll think like a researcher, build like a software engineer, and deliver like a product owner.

You'll have the autonomy to:

  • Design technical solutions from first principles
  • Define success metrics
  • Build production\-ready AI infrastructure
  • Run experiments at scale
  • Make architectural decisions
  • Own systems from idea to deployment.

What You'll Be Building

You'll help create the intelligence layer powering next\-generation AI agents capable of handling sophisticated accounting workflows.

Your work will include:

Designing multi\-agent AI systems that automate complex financial processes

Building reasoning pipelines that allow AI agents to plan, make decisions, and collaborate

Architecting retrieval systems and contextual memory that improve agent performance

Developing evaluation frameworks to benchmark models, measure quality, detect regressions, and continuously improve performance

Optimizing inference, latency, cost, accuracy, and reliability in production

Building validation layers, guardrails, and safety mechanisms for dependable AI behavior

Running structured experiments that drive measurable product improvements rather than relying on intuition

Every feature you build will directly impact production AI systems used in real\-world accounting workflows.

Who We're Looking For

We're looking for engineers who enjoy solving difficult engineering problems—not just training models.

Ideal candidates have:

4–12 years of Machine Learning experience

✔ Strong Python expertise

✔ Hands\-on experience building LLM\-powered applications

✔ Experience with AI Agents, prompt engineering, retrieval systems, model orchestration, evaluation frameworks, benchmarking, and production ML systems

✔ Experience designing experiments, measuring outcomes, and iterating using data

✔ Background in high\-growth startups, AI\-native companies, leading technology firms, or similarly fast\-paced engineering environments

Most importantly, we're looking for builders who enjoy taking ownership and delivering production\-ready AI systems that create measurable business impact.

Tech Stack

  • Python
  • PostgreSQL
  • Large Language Models (LLMs)
  • AI Agents
  • Prompt Engineering
  • Retrieval\-Augmented Generation (RAG)
  • Model Evaluation \& Benchmarking
  • Production ML Infrastructure

Why Join?

You'll be joining one of the fastest\-growing AI startups working at the intersection of artificial intelligence and finance.

  • Build AI products solving real\-world challenges
  • Work alongside exceptional engineers and founders
  • Own high\-impact technical initiatives
  • Competitive salary ($175K\+)
  • Meaningful equity
  • Visa sponsorship available
  • Opportunity to shape the future of Applied AI in accounting and finance

If you're excited about taking AI beyond demos and into production, this role offers the chance to build systems that will redefine how knowledge work gets done.

\#Hiring \#AppliedMachineLearning \#MachineLearning \#LLM \#LargeLanguageModels \#AIAgents \#GenerativeAI \#ArtificialIntelligence \#Python \#MLOps \#PromptEngineering \#RAG \#ProductionAI \#MLInfrastructure \#DeepLearning \#TechJobs \#NYCJobs \#StartupHiring \#EngineeringJobs \#VisaSponsorship \#HiringNow

Pay: From $175,000\.00 per year

Application Question(s):

  • Do you have 4\+ years of experience building production ML systems using Python, LLMs, AI Agents, RAG, prompt engineering, and evaluation frameworks? (Yes/No) \*\*Mandatory\*\*
  • Do you required visa sponsorship? (Yes/No) \*\*Mandatory\*\*
  • What is your current compensation (Per Anum) ? \*\*Mandatory\*\*
  • What is your expected compensation (Per Anum) ? \*\*Mandatory\*\*
  • if you didn't mention your LinkedIn in resume, Please your link below. \*\*Mandatory\*\*

Education:

  • Bachelor's (Required)

Work Location: In person

Role Details

Company carnaby fox
Title Member of Technical Staff – Applied Machine Learning (AI Agents | LLMs | Production AI)
Location Margaretville, NY, US
Experience Senior
Salary Not disclosed
Remote No

About This Role

AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.

Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.

Across the 3,708 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At carnaby fox, this role fits into their broader AI and engineering organization.

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

What the Work Looks Like

A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

Skills Required

Prompt Engineering (15% of roles) Python (51% of roles) Rag (23% of roles)

Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.

The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?

Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.

Compensation Benchmarks

AI Agent Developer roles pay a median of $238,500 based on 58 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.

Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.

carnaby fox AI Hiring

carnaby fox has 2 open AI roles right now. They're hiring across Prompt Engineer, AI Agent Developer. Positions span San Francisco, CA, US, Margaretville, NY, US.

Location Context

Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).

Career Path

Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.

From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.

Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.

What to Expect in Interviews

Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.

When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.

AI Hiring Overview

The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.

The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).

AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.

The AI Job Market Today

The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 58 roles with disclosed compensation, the median salary for AI Agent Developer positions is $238,500. Actual compensation varies by seniority, location, and company stage.
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
About 14% of the 3,708 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
carnaby fox is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Agent Developer positions include AI Architect, Principal Engineer, Head of AI Engineering. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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