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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: Senior* *Specialist, Artificial Intelligence, AI Solutions Developer**Job Code: 40344**Job Location:* *Palm Bay, FL**Job Schedule:* *9/80* Job Description:
L3Harris is seeking a skilled and motivated AI Developer to build AI\-powered solutions that drive efficiency, automation, and data\-driven decision\-making across Segment Operations, including manufacturing, test, quality, and supply chain functions. This role focuses on hands\-on development within Palantir Foundry, Codex and Microsoft Copilot, supporting production AI assistants, workflows, and integrations that directly improve operational performance. As AI adoption is still new and emerging in the workforce, this role will also combine technical tasking with employee engagement, training and AI transformation adoption. The ideal candidate has strong technical foundations, experience in Foundry or Codex, and a willingness to dive deep into both AI tooling and operational business processes. Essential Functions:* Develop, test, deploy, and maintain AI applications, agents, and automations to support manufacturing, test, quality, and supply chain workflows
- Build effective prompt strategies, prompt chains, and standard prompts to streamline operational use cases
- Troubleshoot and enhance production AI solutions, ensuring reliability, accuracy, and performance across operational environments
- Integrate Foundry\-based applications with enterprise systems to support real\-time data access and decision workflows
- Work closely with Operations teams to understand process needs and translate them into technical AI solutions
- Deliver internal training sessions, workshops, and office hours to enable employee adoption of L3Harris AI tools
- Collaborate with data engineers and domain experts to ensure high\-quality data pipelines and ontology alignment with AI ready data
- Contribute to internal AI standards, documentation, and development practices
- Stay current on emerging AI technologies and identify opportunities to apply them to operational challenges
Qualifications:* Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field
- 3–7 years of experience in software development, data engineering, AI/ML engineering, or similar technical roles
- Demonstrated experience developing solutions in Palantir Foundry (transform code, ontology integration, pipelines, code workbooks, contour, etc.)
Strong programming skills in Python, Java, or related languages
*
- 1\+ years of experience implementing production\-grade AI/LLM workflows (prompting, orchestration, agent design)
Preferred Additional Skills:* Ability to quickly learn new tools and technologies and apply them in fluid, fast\-paced environments
- Strong analytical and problem\-solving abilities with attention to detail
- Strong communication skills with the ability to present technical concepts to nontechnical audiences.
- Experience working within manufacturing, test engineering, quality engineering, or supply chain domains
- Experience integrating AI solutions into real\-world operational systems or enterprise tooling
- Palantir Foundry Certification (Builder, Developer, Analyst)
Experience with RAG, embeddings, vector databases, or agentic frameworks (LangChain, LiteLLM, Semantic Kernel)
- Hands\-on experience with cloud platforms (Azure, AWS, GCP)
- Familiarity with DevOps practices, containerization, or infrastructure\-as\-code
- Understanding of secure coding practices and compliance within regulated industries
- Experience building APIs, REST services, or real\-time data integrations
- Familiarity with Microsoft Copilot and modern software development workflows (Git, agile practices)
\#LI\-NB1
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.
L3Harris Technologies is an E\-Verify Employer. Please click here for the E\-Verify Poster in English or Spanish. For information regarding your Right To Work, please click here for English or Spanish.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 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.
L3Harris AI Hiring
L3Harris has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Melbourne, FL, US, Palm Bay, FL, US. Compensation range: $247K - $247K.
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/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 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).
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 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
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