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About This Role
At Zone 5 Technologies, we're redefining what's possible in unmanned aircraft systems. Our team of engineers and innovators is developing cutting\-edge autonomous solutions that push the boundaries of UAS technology \- solving complex challenges that matter.
We're building the future of UAS capabilities, and we're looking for exceptional talent to join us. If you're driven by hard problems, energized by rapid innovation, and ready to make an impact on next\-generation flight systems, you belong here.
We are investing in in\-house LLM tooling and are hiring a dedicated MLOps Engineer to help grow it. You will build AI\-powered capabilities—retrieval\-augmented generation, tool integrations, and agentic workflows—and turn them into reliable services used by teams across the company. This is a builder's role focused on shipping new capability.
*The role spans a broad stack. We welcome both generalists and specialists—you do not need every skill listed below. Tell us where you are strong and where you want to grow. The center of gravity is LLM application development, retrieval quality, and agent design.*
Responsibilities:
LLM Applications, RAG \& Agents
- Design and build new LLM\-powered tools and agentic workflows that automate real work and improve productivity across the company
- Extend and improve our RAG systems—ingestion, chunking, embedding, retrieval, ranking, and evaluation—to raise answer quality
- Structure retrieval around the organization's information hierarchy so that relevance and access boundaries improve together
- Build tool integrations that connect LLMs to internal systems and data sources
- Design agents that act safely against real systems, with appropriate guardrails, human\-in\-the\-loop where warranted, and clear failure behavior
- Establish evaluation and testing frameworks to measure quality, catch regressions, and guide iteration
- Partner with teams across the company to identify high\-value use cases and turn them into deployed tools
Service Deployment \& AI Infrastructure
- Deploy AI tools and services for teams across the company, taking them from prototype to reliable production
- Build and operate the infrastructure that hosts models, tools, and supporting services on Kubernetes
- Manage model serving, inference endpoints, and the APIs and gateways around them
- Implement monitoring, logging, and usage observability so we understand how tools perform and get used
Access, Security \& Data Boundaries
- Ensure retrieval and agent tools respect the same access boundaries as the underlying systems—no cross\-team or cross\-project data leakage
- Integrate with existing identity and permission systems so tools honor who is allowed to see what
- Apply data\-handling practices appropriate to a defense environment
- Treat access control as a first\-class design concern in every tool, not an afterthought
Automation \& Data Operations
- Build CI/CD pipelines for AI tools, services, and agents
- Automate provisioning and configuration with Ansible and infrastructure\-as\-code practices
- Build data pipelines to ingest, transform, and index content for RAG and AI applications
- Manage vector databases and other stores backing retrieval and AI workloads, including versioning and quality checks
- Maintain reproducible environments across development, staging, and production
Qualifications:
- Bachelor's in Computer Science, Software Engineering, Data Engineering, or related field – equivalent industry experience also welcome
- 3\-6\+ years of experience in MLOps, software, platform, or backend engineering (relevant depth matters more than exact years)
- Strong proficiency in Python and comfort building, shipping, and operating services
- Experience building LLM\-powered applications—working with LLM APIs or self\-hosted models, prompts, and tool/function calling
- Hands\-on experience with Kubernetes and containerized deployment
- Solid understanding of CI/CD, infrastructure\-as\-code, and production service reliability
- Awareness of access control and data\-boundary concerns when connecting tools to sensitive internal systems
- Demonstrated ability to learn quickly and work across unfamiliar parts of the stack
- Depth in at least one core area—LLM application development, RAG/retrieval, agent design, or AI infrastructure—with genuine interest in growing into the others
Preferred:
- Hands\-on experience with RAG systems, embeddings, and vector databases (pgvector, Qdrant, Weaviate, Milvus, or similar)
- Experience designing and shipping agentic workflows, including tool use, orchestration, and guardrails
- Familiarity with the Model Context Protocol (MCP) or similar tool\-integration frameworks for LLMs
- Experience integrating LLM tools with enterprise systems (productivity suites, business systems, or developer platforms) via their APIs
- Knowledge of LLM evaluation, prompt engineering, and quality/regression measurement
- Experience serving models and optimizing inference (vLLM, TGI, Triton, or similar)
- Familiarity with agent/orchestration libraries (LangChain, LlamaIndex, or equivalent)
- Experience with Ansible for configuration management and automation
- Experience implementing identity, authentication, and fine\-grained authorization (OAuth, SSO, RBAC)
- Observability experience for AI/ML workloads, including usage and quality metrics
- GPU infrastructure and scheduling experience for training or inference
- Understanding of security and data\-handling requirements in regulated or defense environments
- Ability to obtain or maintain a security clearance
What's in it for you:
Benefits:
- Competitive total compensation package
- Comprehensive benefit package options include medical, dental, vision, life, and more.
- 401k with company\-match
- 4 weeks of paid time off each year
- 12 annual company holidays
Why Join Zone 5 Technologies?
- Innovative Environment: Work on cutting\-edge technology that is shaping the future of defense and aerospace.
- Collaborative Culture: Join a team of passionate professionals dedicated to pushing the boundaries of what's possible.
- Career Growth: Opportunities for professional development and career advancement.
If you are passionate about unmanned aircraft technology and want to be a part of a dynamic and growing company, we would love to hear from you. Apply today and join the Zone 5 Technologies team!
*In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.*
*Zone 5 Technologies is a federal contractor and participates in E\-Verify to confirm employment eligibility. As required by law, we will verify the identity and employment authorization of all new employees using the E\-Verify system. Learn more about your rights and responsibilities under E\-Verify:* *https://www.e\-verify.gov**.*
Salary Context
This $140K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Zone 5 Technologies, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($157K) sits 27% below the category median. Disclosed range: $140K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Zone 5 Technologies AI Hiring
Zone 5 Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $175K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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