Posted:16 hours ago|
Platform:
On-site
Full Time
We're looking for a passionate Machine Learning Engineer with a strong foundation in computer vision, model training and deployment, observability pipelines, and DevOps for AI systems. You’ll play a pivotal role in building scalable, accurate, and production-ready ML systems in a collaborative and distributed environment. Responsibilities Train, fine-tune, and evaluate object detection and OCR models (YOLO, GroundingDINO, PP-OCR, TensorFlow). Build and manage observability pipelines for evaluating model performance in production (accuracy tracking, drift analysis). Develop Python-based microservices and asynchronous APIs for ML model serving and orchestration. Package and deploy ML services using Docker and Kubernetes across distributed environments. Implement and manage distributed computing workflows with NATS messaging. Collaborate with DevOps to configure networking (VLANs, ingress rules, reverse proxies) and firewall access for scalable deployment. Use Bash scripting and Linux CLI tools (e.g., sed, awk) for automation and log parsing. Design modular, testable Python code using OOP and software packaging principles. Work with PostgreSQL, MongoDB, and TinyDB for structured and semi-structured data ingestion and persistence. Manage system processes using Python concurrency primitives (threading, multiprocessing, semaphores, etc.). Required Skills Languages: Python (OOP, async IO, modularity), Bash Computer Vision: OpenCV, Label Studio, YOLO, GroundingDINO, PP-OCR ML & MLOps: Training pipelines, evaluation metrics, observability tooling DevOps & Infra: Docker, Kubernetes, NATS, ingress & firewall configs Data: PostgreSQL, MongoDB, TinyDB Networking: Subnetting, VLANs, service access, reverse proxy setup Tools: sed, awk, firewalls, reverse proxies, Linux process control
Alvyl Consulting
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