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About This Role
The Agent Developer I is an entry\-level role designed for early‑career technologists ready to build the next generation of autonomous software systems. In this position, you will help design, develop, and deploy intelligent agents capable of multi\-step reasoning, tool use, and decision\-making with minimal human oversight. This role blends software engineering, systems design, and large language model (LLM) orchestration to create agents and multi\-agent systems that can autonomously execute tasks, interact with external tools and APIs, and adapt to dynamic, real‑world environments. It’s an exciting opportunity for candidates passionate about emerging technologies who want to grow their technical skill set and contribute to impactful, mission\-driven projects in a collaborative environment.
As SNC's corporate team, we provide the company and its business areas with strategic direction and business support spanning executive management, finance and accounting, operations, human resources, legal, IT, information security, facilities, marketing, and communications.
Responsibilities:
- Stay up\-to\-date with the latest advancements in AI/ML technologies by participating in training, workshops, and team discussions..
- Develop and implement AI agents and multi‑agent systems capable of autonomously executing tasks, making context‑driven decisions, and interacting with external tools, APIs, databases, and external services.
- Architect and orchestrate multi‑step, multi‑agent workflows, decomposing high‑level goals into executable tasks and ensuring seamless communication, reliable tool use, and efficient coordination across complex pipelines.
- Implement logic for LLM‑driven decision‑making, including tool selection, tool chaining, fallback strategies, guardrails, and overall agent resilience.
- Build and maintain evaluation and observability frameworks to measure agent accuracy, reliability, and efficiency, while continuously monitoring production behavior and improving system robustness.
- Develop memory, retrieval, and RAG systems using vector databases or knowledge graphs to enhance agent context, reasoning, and long‑term performance.
- Deploy, scale, and manage agent‑based solutions on cloud platforms such as AWS, Azure, and GCP.
Collaborate with cross‑functional teams—including product, domain experts, engineering, and security—to ensure agent capabilities align with business requirements and operational constraints.
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Qualifications You Must Have:
- Bachelor’s degree in computer science, mathematics, applied statistics, various engineering disciplines, or related STEM discipline
- 0\-2 years of experience in cloud engineering or a related field.
- Relevant experience can be considered as a substitute for the required educational qualifications. In the absence of a degree, a minimum of 4 years of related experience is required.
- Basic programming skills in Python, C\+\+, C\# or Java.
- Understanding of foundational machine learning concepts and techniques.
- Strong analytical and problem\-solving skills.
- Proficiency in prompt engineering and context design for reasoning and tool orchestration
API and tool integration skills (REST, GraphQL, microservices)Strong analytical and problem\-solving skills.
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Qualifications We Prefer:
- Exposure to neural networks, signal processing, or computer vision frameworks.
- Internship or project\-based experience in AI, data science, or software development.
- Familiarity with version control tools (e.g., Git).
- Exposure to Agile or DevOps methodologies through coursework or internships.
- Experience participating in team\-based software development projects.
Ability to analyze small\-to\-moderate datasets using standard statistical or ML methods.
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Essential Functions:
- Work on a computer for extended hours performing coding, debugging, and testing.
- Attend and contribute to team meetings and brainstorming sessions.
- Travel occasionally to support testing or deployment efforts (up to 10%).
- Ability to work in a hybrid office environment and adapt to project requirements.
This posting will be open for application for a minimum of 5 days and may be extended based on business needs.
SNC offers a generous benefit package, including medical, dental, and vision plans, 401(k) with 150% match up to 6%, life insurance, 3 weeks paid time off, tuition reimbursement, and more .
IMPORTANT NOTICE:
To conform to U.S. Government international trade regulations, applicant must be a U.S. Citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3\), or eligible to obtain the required authorizations from the U.S. Department of State or U.S. Department of Commerce.
SNC is a global leader in aerospace and national security committed to moving the American Dream forward. We’re known and respected for our mission and execution focus, agility, and disruptive and rapid innovation. We provide leading edge technologies and transformative solutions that support our nation’s most critical security needs. If you are mission\-focused, thrive in collaborative environments, and want to make our country stronger with state\-of\-the\-art technologies that safeguard freedom, join our team!
SNC is an Equal Opportunity Employer committed to an environment free of discrimination. Employment decisions are made based on merit without regard to race, color, age, religion, sex, national origin, disability, status as a protected veteran or other characteristics protected by law.
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 Sierra Nevada Corporation, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Sierra Nevada Corporation AI Hiring
Sierra Nevada Corporation has 5 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Positions span Sparks, NV, US, Lone Tree, CO, US, Remote, US. Compensation range: $98K - $171K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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