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About This Role
Complete Weddings and Events is North America’s largest and most successful special event service. Complete Media Group works to make those weddings and special events memorable with extraordinary post production.
We are seeking a detail\-oriented, tech\-savvy AI Photo Editing Specialist to spearhead our high\-volume post\-production workflow. In this role, you will bridge the gap between cutting\-edge AI technology and high\-end creative execution. You will be responsible for training, managing, and refining AI editing profiles (e.g., Aftershoot, ImagenAI, Lightroom AI) to ensure they perfectly match our brand’s distinct visual styles—ranging from clean and natural to moody and dramatic.
Beyond automated culling and editing, you will serve as the ultimate line of defense for quality control, performing manual touch\-ups, managing large\-scale data workflows, and constantly optimizing our post\-production pipeline for speed and flawless consistency.
Qualifications and Skills:
- Proficient in Adobe Lightroom Classic and Adobe Photoshop photo editing software and AI Platforms
- Ability to work independently from your home office or remote location
- Strong communication skills and able to handle constructive criticism
- Maintain tight deadlines
- Exceptional time management
- Passion for photography \& editing
- Unafraid to ask questions and receive feedback
Job Responsibilities:
- Build, train, and continuously refine AI smart profiles based on historical catalog data and specific style guides (Natural, Moody, Bright, etc.).
- Oversee the automated culling and first\-pass editing of thousands of incoming images per week.
- Manage seamless file transfers and data pipelines between cloud storage, local servers, and AI software.
- Troubleshoot technical bottlenecks in the AI uploading, chunking, and rendering processes.
- Review 100% of AI\-processed sets to catch anomalies, masking errors, unnatural skin tones, or artifacting.
- Perform skilled manual corrections in Adobe Lightroom Classic and Photoshop (advanced masking, color matching, cropping, and object removal) where AI falls short.
- Ensure final galleries meet strict internal quality benchmarks before delivery.
- Stay ahead of industry trends by testing new AI tools, plugins, and beta features to improve turnaround times.
- Collaborate with department managers to document standard operating procedures (SOPs) for the post\-production team.
- Monitor AI editing outputs for style drift and adjust model parameters to maintain strict visual consistency across different lighting conditions and camera bodies.
Core Competencies
- An Exceptional Eye: A sophisticated understanding of color theory, white balance, exposure, and composition. The ability to spot a 50K temperature variance from a mile away.
- Systems Thinker: Someone who enjoys optimizing processes, building recipes/presets, and finding a more efficient way to handle repetitive tasks.
- High Attention to Detail: Thrives in a high\-volume environment without sacrificing the quality of individual assets.
- AI Tool Proficiency: Proven experience working with AI culling and editing software (e.g., Aftershoot, ImagenAI, or Lightroom's native AI masking and generative erase tools).
- Data \& Workflow Savvy: Familiarity with high\-volume digital asset management (DAM), cloud storage platforms, and workflow automation tools.
Success will be measured by
- Turnaround Time (TAT): Average time from raw file ingest to final QA\-approved gallery.
- AI Accuracy Rate: The percentage of images that require zero or minimal manual intervention after the AI pass.
- QA Rejection Rate: How many images are flagged for re\-editing by department heads or clients.
- Volume Throughput: Number of images successfully processed and finalized per week/month.
Job Types: Contract, Temporary
Pay: From $18\.00 per hour
Benefits:
- Flexible schedule
Education:
- High school or equivalent (Preferred)
Experience:
- Wedding Photography: 1 year (Required)
- Adobe Lightroom: 1 year (Required)
Work Location: Remote
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Complete Post-production, 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 $181,170 based on 12,692 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.
Complete Post-production AI Hiring
Complete Post-production has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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).
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 3,823 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.
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