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About This Role
AI/ML Engineer with Drone Imagery
Bright Vision Technologies is a forward\-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting\-edge technologies to create scalable, secure, and user\-friendly applications. As we continue to grow, we’re looking for a AI/ML Engineer with Drone Imagery to join our dynamic team and contribute to our mission of transforming business processes through technology.
This is a fantastic opportunity to join an established and well\-respected organization offering tremendous career growth potential.
Job Title: AI/ML Engineer with Drone Imagery
Job Location: Houston, TX 77024\.(Remote)
Job Type: Contract
Bright Vision Technologies is seeking an experienced AI/ML Engineer with Drone Imagery experience for a long\-term hybrid contract opportunity. The ideal candidate will have strong hands\-on experience in machine learning, computer vision, drone imagery, aerial image analytics, anomaly detection, object detection, image segmentation, and scalable image data processing.
This role will focus on accelerating automation across drone programs by extracting and structuring data from drone models, applying AI/ML models to detect anomalies from drone imagery, building scalable drone photo storage and management solutions, and supporting autonomous or semi\-autonomous drone operations.
The ideal candidate should have experience working with drone imagery or similar visual datasets involving towers, buildings, vertical structures, ground placement environments, telecom assets, infrastructure inspection, construction sites, utilities, or industrial assets.
Key Responsibilities* Design, develop, and deploy AI/ML models for drone imagery analysis, aerial image processing, and automated visual inspection use cases.
- Extract, clean, classify, and structure data from drone models, aerial images, 3D scans, photogrammetry outputs, and image\-based inspection datasets.
- Build computer vision models to detect anomalies, defects, damages, missing components, structural issues, equipment placement issues, and visual inconsistencies from drone imagery.
- Develop object detection, image segmentation, classification, and pattern recognition models using frameworks such as PyTorch, TensorFlow, OpenCV, YOLO, Detectron, or similar tools.
- Work with drone photo datasets, image metadata, geospatial references, 3D models, LiDAR outputs, and photogrammetry\-based inspection data.
- Design scalable data pipelines for storing, processing, indexing, and retrieving large volumes of drone photos and image\-based inspection records.
- Support automation across drone programs by reducing manual review effort and improving accuracy in image\-based inspection workflows.
- Collaborate with business, engineering, field operations, data science, and platform teams to understand inspection requirements and convert them into AI/ML solutions.
- Develop models and pipelines to support tower, building, vertical structure, ground equipment, utility, infrastructure, or industrial site inspection use cases.
- Build repeatable model training, validation, testing, deployment, and monitoring workflows for drone imagery analytics.
- Perform image preprocessing, annotation review, feature extraction, augmentation, model tuning, and performance evaluation.
- Work with MLOps practices to support model versioning, experiment tracking, CI/CD, monitoring, retraining, and production deployment.
- Design solutions for drone image storage, photo cataloging, metadata tagging, retrieval, and lifecycle management.
- Support autonomous or semi\-autonomous drone operations by contributing to AI\-assisted detection, classification, and decision\-support workflows.
- Evaluate model accuracy, false positives, false negatives, precision, recall, and other performance metrics for computer vision models.
- Collaborate with data engineering teams to integrate drone imagery outputs with enterprise systems, dashboards, reporting platforms, and workflow tools.
- Troubleshoot model performance issues and optimize computer vision pipelines for scalability, speed, accuracy, and cost efficiency.
- Document model logic, data flow, assumptions, limitations, and operational procedures for business and technical stakeholders.
- Ensure AI/ML solutions follow data governance, security, compliance, and responsible AI practices.
Required Qualifications* 8\+ years of overall IT / data / AI / software engineering experience.
- Strong hands\-on experience in machine learning, deep learning, computer vision, and image analytics.
- Practical experience working with drone imagery, aerial imagery, satellite imagery, remote sensing, LiDAR, photogrammetry, 3D models, or large\-scale image datasets.
- Strong programming experience with Python.
- Hands\-on experience with AI/ML frameworks such as PyTorch, TensorFlow, Keras, OpenCV, Scikit\-learn, YOLO, Detectron, Mask R\-CNN, or similar tools.
- Experience building object detection, image segmentation, classification, anomaly detection, and visual inspection models.
- Experience processing large image datasets, including image cleaning, labeling, augmentation, feature extraction, and metadata management.
- Experience with scalable data storage and processing for drone photos, image datasets, geospatial data, or inspection records.
- Strong understanding of model training, validation, testing, deployment, and monitoring.
- Experience with MLOps tools and practices such as MLflow, Docker, Kubernetes, CI/CD, model registry, model monitoring, or automated retraining.
- Experience working with cloud platforms such as AWS, Azure, or GCP.
- Ability to work with cross\-functional teams including field operations, engineering, inspection teams, data engineers, and business stakeholders.
- Strong analytical, problem\-solving, and communication skills.
Preferred Qualifications* Experience with tower inspections, telecom infrastructure, utility inspections, construction site inspections, energy assets, industrial inspection, buildings, vertical structures, or ground placement environments.
- Experience with drone platforms, drone inspection workflows, drone photo management, or drone program automation.
- Experience with GIS, geospatial data, spatial indexing, mapping systems, or location\-based analytics.
- Experience with LiDAR point clouds, 3D reconstruction, photogrammetry tools, or digital twin environments.
- Experience with Databricks, Snowflake, Spark, or distributed data processing platforms.
- Experience deploying AI/ML models into enterprise production environments.
- Experience building APIs or services to expose ML model outputs to business applications.
- Experience with annotation platforms, data labeling workflows, active learning, or synthetic data generation.
- Prior experience in telecom, wireless infrastructure, construction, energy, utilities, engineering, or industrial asset inspection domains.
Primary Skills:
AI/ML, Machine Learning, Deep Learning, Computer Vision, Drone Imagery, Aerial Imagery, Image Processing, Object Detection, Image Segmentation, Anomaly Detection, Python, PyTorch, TensorFlow, OpenCV
Would you like to know more about this opportunity? For immediate consideration, please send your resume directly to [email protected] or contact us via phone/text/whatsapp at \+1 908\.505\.3899
At BVTeck, we are committed to providing equal employment opportunities and fostering an inclusive work environment. We encourage applications from all qualified individuals regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or any other protected status. If you require accommodations during the recruitment process, please let us know.
Position offered by “No Fee agency.”
Equal Employment Opportunity (EEO) Statement
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
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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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BV Teck, 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 $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
BV Teck AI Hiring
BV Teck has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Schaumburg, IL, US, Remote, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,883 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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