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About This Role
L3Harris is dedicated to recruiting and developing high-performing talent who are passionate about what they do. Our employees are unified in a shared dedication to our customers’ mission and quest for professional growth. L3Harris provides an inclusive, engaging environment designed to empower employees and promote work-life success. Fundamental to our culture is an unwavering focus on values, dedication to our communities, and commitment to excellence in everything we do.
L3Harris is the Trusted Disruptor in defense tech. With customers’ mission-critical needs always in mind, our employees deliver end-to-end technology solutions connecting the space, air, land, sea and cyber domains in the interest of national security.
Job Title: Specialist, Artificial Intelligence Engineer
Job Code: 45547
Job Location: Melbourne, FL
Job Schedule: 9/80: Employees work 9 out of every 14 days – totaling 80 hours worked – and have every other Friday off
Job Description:
L3Harris is seeking a highly qualified Artificial Intelligence Engineer who is an experienced professional responsible for developing, implementing, and improving artificial intelligence and automation solutions that support security engineering, infrastructure operations, and systems administration functions. This role applies proficient knowledge of artificial intelligence, machine learning, automation frameworks, IT operations, and engineering practices to enhance operational efficiency, strengthen security posture, and improve the effectiveness of enterprise infrastructure and administrative processes.
The position supports the design and deployment of AI-enabled tools and automated workflows for use cases such as operational monitoring, incident response, vulnerability analysis, alert triage, configuration management, systems health assessment, log analysis, and administrative task automation. The engineer works under general supervision and may be responsible for entire projects or processes within the job area, including coordinating technical activities, contributing to project execution, and reviewing work products.
This role requires regular collaboration with cybersecurity teams, infrastructure engineers, systems administrators, software engineers, and other internal stakeholders to explain technical processes, interpret operational procedures, and deliver scalable solutions aligned with organizational objectives. The Specialist, Artificial Intelligence Engineer also identifies opportunities to enhance systems and processes, solves moderately complex technical problems using prior experience and analysis, and contributes to operational targets with moderate impact on departmental results.
- This is a 100% onsite position at L3Harris offices located in Melbourne, Florida and cannot be performed remotely.
- This position requires the ability to obtain Public Trust suitability.
Essential Functions:
- Design, develop, test, and implement AI-enabled tools and automation solutions supporting security engineering, infrastructure operations, and systems administration.
- Build and maintain machine learning and rule-based automation workflows for operational use cases such as alert correlation, event analysis, anomaly detection, incident enrichment, and task automation.
- Develop solutions that improve efficiency in areas such as system monitoring, log analysis, patch verification, compliance checks, account administration, and infrastructure support processes.
- Partner with security engineers, infrastructure teams, and system administrators to identify automation opportunities and translate operational needs into technical solutions.
- Support the development and integration of AI/ML models, data pipelines, and automation scripts into enterprise or mission support environments.
- Analyze operational data from systems, applications, networks, and security tooling to identify patterns, improve visibility, and support proactive response actions.
- Recommend and implement moderate enhancements to systems, workflows, and engineering processes to improve effectiveness within the job area.
- Contribute to projects involving automation of routine administrative tasks, infrastructure diagnostics, security operations workflows, and operational reporting.
- Assist with model evaluation, system testing, validation, and performance tuning to ensure solutions meet operational and technical requirements.
- Create and maintain technical documentation including workflow descriptions, architecture diagrams, operating procedures, and implementation guidance.
- Communicate with stakeholders inside and outside the department to explain technical capabilities, processes, and procedures and to support adoption of developed solutions.
- Provide guidance, coaching, and training to other employees within the job area, as needed.
- Review technical work products for quality, completeness, and alignment to project or operational objectives.
- Ensure solutions are developed and implemented in accordance with applicable security, compliance, and configuration management requirements.
Qualifications:
- Bachelor’s degree with a minimum of 4 years of relevant experience, or a Graduate degree with 2 years of relevant experience. In lieu of a degree, 8 or more years of directly related experience will be considered.
- Experience developing automation or AI/ML solutions using Python, PowerShell, Bash, or similar scripting/programming languages.
- Experience supporting security operations, infrastructure management, enterprise systems, or systems administration environments.
Preferred Additional Skills:
- Familiarity with operational data sources such as system logs, security alerts, asset data, telemetry, configuration data, or infrastructure performance metrics.
- Ability to work under general supervision while managing assigned projects, tasks, or processes within the job area.
- Ability to analyze operational problems and develop practical technical solutions that improve efficiency, reliability, or security.
- Experience communicating technical processes and procedures to internal stakeholders and collaborating across engineering and operations teams.
- Practical knowledge of project management and experience contributing to the successful execution of technical projects.
- Strong written and verbal communication skills and ability to document technical solutions clearly and effectively.
- Experience in applying AI, machine learning, or intelligent automation to cybersecurity, infrastructure operations, or systems administration use cases.
- Experience with security engineering disciplines such as vulnerability management, endpoint security, security monitoring, identity and access management, or incident response.
- Experience with infrastructure operations including server administration, virtualization, storage, network operations, cloud services, or platform support.
- Familiarity with systems administration tasks across Windows, Linux, and enterprise infrastructure environments.
- Experience with automation frameworks and tools such as Ansible, Terraform, orchestration platforms, or workflow automation tools.
- Experience with SIEM, SOAR, log aggregation, monitoring, observability, or ticketing platforms.
- Familiarity with machine learning frameworks, data analysis libraries, and workflow orchestration technologies.
- Experience developing solutions for anomaly detection, alert prioritization, root cause identification, predictive maintenance, or intelligent operational support.
- Ability to recommend and implement process improvements that enhance security posture, reduce manual effort, and improve operational consistency.
- Experience reviewing, validating, and improving technical work products developed by other team members.
- Familiarity with secure development practices, access control requirements, and compliance-driven operating environments.
- Prior experience in aerospace, defense, or highly regulated enterprise environments is preferred.
#Li-VT1
L3Harris Technologies is proud to be an Equal Opportunity Employer. L3Harris is committed to treating all employees and applicants for employment with respect and dignity and maintaining a workplace that is free from unlawful discrimination. All applicants will be considered for employment without regard to race, color, religion, age, national origin, ancestry, ethnicity, gender (including pregnancy, childbirth, breastfeeding or other related medical conditions), gender identity, gender expression, sexual orientation, marital status, veteran status, disability, genetic information, citizenship status, characteristic or membership in any other group protected by federal, state or local laws. L3Harris maintains a drug-free workplace and performs pre-employment substance abuse testing and background checks, where permitted by law.
Please be aware many of our positions require the ability to obtain a security clearance. Security clearances may only be granted to U.S. citizens. In addition, applicants who accept a conditional offer of employment may be subject to government security investigation(s) and must meet eligibility requirements for access to classified information.
By submitting your resume for this position, you understand and agree that L3Harris Technologies may share your resume, as well as any other related personal information or documentation you provide, with its subsidiaries and affiliated companies for the purpose of considering you for other available positions.
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At L3Harris, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
L3Harris AI Hiring
L3Harris has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Melbourne, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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