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About This Role
Headquartered in Birmingham, Alabama, Moultrie (www.moultrie.com) is the leader in game feeders and cellular camera innovation, building products used by hunters, property owners, and others for real\-time remote monitoring.
We take pride in developing deep user understanding, obsessing about the details, and going the extra mile to show our users we love them. Moultrie is customer\-driven – hardware, software, marketing, and customer success teams collaborate to deliver a quality user experience.
We are guided by the following principles: Customer Obsession.; Excellence is the Standard.; Bias for Action.; Act Boldly.; Deliver Results.; Hire and Develop the Best.; Be Curious and Learn.; Win as a Team
Job Summary
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The AI Development Advocate is a strategic, hands\-on leader responsible for scaling AI\-driven developer productivity across the organization. Serving as Moultrie's internal Developer Relations (DevRel) lead for AI, this role owns the end\-to\-end strategy and execution for transitioning our software development teams to an AI agent\-first model. Think of it as an internal Developer Experience (DX) function: your customers are our engineers, and your success is measured by their productivity, confidence, and depth of AI adoption. Your mandate is to maximize AI impact at scale, from onboarding new teams to advancing the most experienced practitioners.
You will set the strategic direction for AI\-assisted development at Moultrie while remaining deeply hands\-on, building tools, running workshops, unblocking teams, and leading by example. This role bridges cutting\-edge AI capabilities and practical software delivery, scaling enablement through a combination of direct support, structured programs, and an internal Champions community. Success here requires equal parts technical credibility, program discipline, and the ability to inspire engineers at every level to work in fundamentally new ways.
Job Responsibilities
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- Define and own the organization\-wide strategy for AI agent\-first development: setting direction, establishing adoption milestones, and driving a measurable, lasting shift in how developers design and ship software.
- Build and maintain internal tooling, reference implementations, and proof\-of\-concept workflows that demonstrate how AI agents can accelerate development tasks such as code generation, code review, testing, and documentation.
- Provide hands\-on technical support to engineering teams encountering issues with AI tools, agent workflows, prompt engineering, or LLM integration; serving as the first point of escalation for AI\-related blockers.
- Design and execute AI onboarding plans for engineering teams (including initial setup, role\-specific training, and structured ramp programs) and provide ongoing guidance as AI development capabilities rapidly evolve.
- Build feedback loops between engineering teams and tech leadership, synthesizing adoption blockers, tool gaps, and team needs into actionable recommendations.
- Build and grow an internal AI Champions community by identifying and enabling enthusiastic early adopters within each team to serve as peer advocates, accelerate grassroots adoption, and scale enablement beyond what a single advocate can reach.
- Define and track AI adoption metrics across teams, measuring tool usage, productivity impact, and developer confidence over time.
- Partner with product and engineering leadership to identify high\-value use cases for AI agent automation within the software development lifecycle (SDLC), from code generation and review to testing and deployment.
- Monitor the AI tool ecosystem and maintain awareness of the latest agentic frameworks, LLM capabilities, and developer tooling trends to ensure our teams remain current and competitive.
- Own AI governance and cost controls, establishing usage policies, license management, spend visibility, and guardrails that ensure responsible and cost\-effective AI adoption across the engineering organization.
Job Requirements
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- Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
- 5\+ years of hands\-on software development experience, with a strong foundation in modern software engineering practices.
- Demonstrable hands\-on experience using AI\-assisted development tools (including GitHub Copilot) to improve personal and team productivity in a professional software development context.
- Proficiency in .NET, Java, or a similar language; comfortable writing, reviewing, and guiding production\-quality code across engineering teams.
- Experience creating and delivering technical presentations, workshops, or training sessions to developer audiences.
- Experience with cloud platforms (AWS, Azure, or GCP) and integrating AI services into software pipelines and CI/CD workflows.
Preferred Qualifications
- Advanced experience with GitHub Copilot, including Copilot Chat, Copilot for CLI, and agent\-mode workflows; familiarity with GitHub Copilot administration and license management.
- Familiarity with prompt engineering techniques and AI agent patterns as applied to developer productivity use cases (e.g., code generation, automated testing, documentation, code review).
- Prior experience in a Developer Relations (DevRel), Developer Experience (DX), technical enablement, or internal engineering advocacy role, with a track record of driving tool adoption and improving the developer experience at scale.
- Strong technical writing skills: able to produce internal documentation, how\-to guides, and best\-practice references that engineers will actually use.
- Demonstrated experience driving technology adoption and organizational change within engineering organizations.
- Ability to coach and mentor engineers at all levels on AI tooling, agentic patterns, and evolving best practices.
What We Offer
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- Competitive salary commensurate with experience, with access to cutting\-edge AI tooling and infrastructure.
- Full\-time position with a front\-row seat to our AI transformation across all engineering teams.
- Collaborative, cross\-functional environment with high visibility to engineering leadership and direct impact on developer productivity.
We are an equal opportunity employer and comply with all applicable federal, state, and local fair employment practices laws. We strictly prohibit and do not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, sex, pregnancy status, age, national origin or ancestry, ethnicity, religion, creed, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.
We comply with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local law.
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 PRADCO, 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.
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.
PRADCO AI Hiring
PRADCO has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Birmingham, AL, US, MA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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