- Own the end-to-end MLOps lifecycle, from model packaging and CI/CD to deployment, monitoring, and rollback for computer vision, NLP, and multi-modal models.
- Design and maintain scalable training and inference pipelines for large datasets and models, optimizing for cost, latency, and throughput.
- Build and manage containerized deployment infrastructure (Docker, Kubernetes) for hosted deep learning and geoprocessing services.
- Set up and maintain experiment tracking, model registry, and versioning systems to ensure reproducibility across the research-to-production lifecycle.
- Implement model monitoring and observability — drift detection, performance degradation alerts, logging, and dashboards, for models running in production.
- Apply model optimization techniques (quantization, pruning, knowledge distillation) to improve inference efficiency in production.
- Collaborate with Research Engineers, Backend Engineers, and Product teams to translate research ideas into deployable, production-ready services.
- Develop and maintain infrastructure-as-code, monitoring, and logging for all deployed ML/AI software.
- Evaluate, profile, and continuously improve the reliability, scalability, and cost-efficiency of existing ML systems.
- Stay current with evolving MLOps tooling and best practices and evaluate applicability to construction industry challenges.
- 3+ years of experience in MLOps, ML infrastructure, or applied AI/ML engineering, with exposure to Computer Vision or NLP systems.
- Hands-on experience with workflow orchestration frameworks (preferably Temporal) for building reliable, fault-tolerant, long-running distributed workflows.
- Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, OpenCV, or HuggingFace Transformers.
- Hands-on experience with Docker, Kubernetes, and containerized ML deployment pipelines in production environments.
- Experience building and maintaining CI/CD pipelines for ML systems (e.g., Jenkins, GitHub Actions, GitLab CI).
- Working knowledge of experiment tracking and model registry tools (e.g., MLflow, Weights & Biases, DVC).
- Experience with cloud environments (GCP, AWS, or Azure) and infrastructure-as-code practices.
- Familiarity with messaging and data processing pipelines (e.g., Apache Kafka, RabbitMQ) and distributed web services.
- Understanding of model optimization techniques such as quantization, pruning, and knowledge distillation - Good to have.
- Experience with version control systems (e.g., Git) and project tracking tools (e.g., JIRA).
- Familiarity with PostGIS or other geo-databases and Geographic Information Systems - Good to have.
- Strong analytical and problem-solving skills with a passion for building reliable, scalable systems; comfortable working in a fast-paced, agile, startup-like environment.
Skills Required
- 3+ years of experience in MLOps, ML infrastructure, or applied AI/ML engineering
- Exposure to computer vision or NLP systems
- Hands-on experience with workflow orchestration frameworks, preferably Temporal
- Strong proficiency in Python
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, OpenCV, or HuggingFace Transformers
- Hands-on experience with Docker, Kubernetes, and containerized ML deployment pipelines in production environments
- Experience building and maintaining CI/CD pipelines for ML systems
- Working knowledge of experiment tracking and model registry tools such as MLflow, Weights & Biases, or DVC
- Experience with cloud environments such as GCP, AWS, or Azure
- Experience with infrastructure as code practices
- Familiarity with messaging and data processing pipelines such as Apache Kafka or RabbitMQ
- Familiarity with distributed web services
- Understanding of quantization, pruning, and knowledge distillation
- Experience with Git and project tracking tools such as JIRA
- Familiarity with PostGIS or other geodatabases and Geographic Information Systems
- Strong analytical and problem-solving skills
- Ability to work in a fast-paced, agile, startup-like environment
What We Do
Attentive.ai is the #1 landscape management software provider with end-to-end automation for all field services businesses across the landscaping, asphalt and paving, facilities maintenance, and snow removal industry. Our software- powered by cutting-edge Artificial Intelligence (AI), is designed to optimize your workflows and help you scale effortlessly. Our property measurement software, Automeasure, caters to landscaping maintenance and construction, paving maintenance and construction, facilities maintenance, and snow removal businesses. Automate your takeoffs on up-to-date aerial imagery and blueprints, helping sales teams save time, bid more, and win more. Our landscape management software, Accelerate, arms you with a truly end-to-end solution for all commercial landscape maintenance and construction jobs through automated workflows. Over 500 businesses across the field services industry—landscaping, snow management, paving maintenance, construction, and facilities maintenance industries in the US and Canada trust Attentive.ai to drive their revenue. This includes the most successful sales teams, like those at U.S. Lawns, Juniper Landscaping, Maldonado Nursery & Landscaping, United Land Services, Elements Mountain Company, Beary Landscaping, Paved Assets, East Coast Facilities, BrightView, Case Snow, and LandCare. Backed by marquee investors, including Sequoia Surge and InfoEdge Ventures, we are building to solve the biggest challenges facing the outdoor services businesses.
