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About This Role
About Level AI
Level AI is on a mission to turn every customer interaction into a strategic advantage. Our AI\-native platform helps enterprises transform contact centers from cost centers into engines of customer intelligence, operational efficiency, and business growth. By combining advanced AI with deep domain understanding of customer experience, Level AI empowers teams to unlock actionable insights, automate workflows, and deliver more consistent, higher\-quality support across the customer journey.
Headquartered in Mountain View, California, Level AI is a Series C company backed by leading investors including Battery Ventures and ENIAC. Our platform leverages Large Language Models and Custom Small Language Models (SLMs) to power AI Agents across the entire CX journey—customer\-facing agents, agent\-assist, and backend automation—along with deep conversation analytics for QA, coaching, and insights.
About the role
As a Senior Engineer – AI Agents, you will play a critical role in building the scalable backend systems that power Level AI’s next\-generation AI Agents. These systems operate in real\-time, high\-volume enterprise environments and are central to delivering intelligent, production\-grade AI experiences.
You will work at the intersection of distributed systems, cloud infrastructure, and AI\-powered applications—bringing agentic AI capabilities into production at scale.
### What you’ll get to do at Level AI (and more as we grow together):
- Design and build scalable backend systems powering AI Agents that operate in real\-time enterprise environments
- Develop agent orchestration frameworks (multi\-step reasoning, tool usage, decisioning workflows)
- Build systems for agent memory, context management, and state persistence across interactions
- Architect low\-latency inference pipelines integrating LLMs, SLMs, and external tools/services
- Implement evaluation (evals) frameworks to measure agent performance, accuracy, and reliability
- Enable continuous improvement loops (feedback retraining deployment) for AI agents in production
- Design and manage event\-driven, asynchronous workflows for complex agent tasks
- Optimize systems for high throughput, low latency, and cost\-efficient inference at scale
- Build and maintain robust APIs and service layers (REST / gRPC) for agent capabilities
- Partner closely with Applied AI / ML teams to productionize models and agent behaviours.
- Collaborate with Product and Solutions teams to translate real customer workflows into agentic systems
- Drive best practices in observability, monitoring, safety, and guardrails for AI systems
- Contribute to architecture decisions for scaling multi\-tenant, enterprise\-grade AI platforms
### We'll love to explore more about you if you have:
- 5\+ years of experience in backend engineering, distributed systems, or platform engineering
- Strong experience building high\-scale, production\-grade backend systems
- Experience designing systems for real\-time processing, streaming, or event\-driven architectures
- Strong understanding of API design (REST, gRPC) and microservices architectures
- Experience with databases (SQL \+ NoSQL) and data modeling for high\-scale systems
- Hands\-on experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure)
- Strong fundamentals in system design, concurrency, and performance optimization
Strong Plus (Agent / AI focus):
- Experience working with LLMs, conversational AI, or AI\-powered products in production
- Familiarity with agent frameworks, tool calling, or multi\-step reasoning systems
- Experience building or integrating RAG pipelines, vector databases, or retrieval systems
- Exposure to evaluation systems (offline/online evals, A/B testing for AI systems)
- Understanding of prompting strategies, context windows, and model behavior optimization
- Experience with real\-time decisioning systems or workflow orchestration engines
Perks \& Benefits
- Competitive compensation with performance\-based upside
- Flexible vacation policy
- Health insurance coverage
- Work with a globally distributed, high\-impact team
- Opportunity to build cutting\-edge AI products at scale
- Regular team offsites and in\-person collaboration
To learn more visit : https://thelevel.ai/
Funding : https://www.crunchbase.com/organization/level\-ai
LinkedIn : https://www.linkedin.com/company/level\-ai/
Our AI platform : https://www.youtube.com/watch?v\=g06q2V\_kb\-s
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Role Details
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 3,823 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Level AI, 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
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 $245,040 based on 106 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Level AI AI Hiring
Level AI has 2 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Positions span Remote, US, Mountain View, CA, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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