Remote Applied Ai/Ml Engineer: Production Systems

The Role:

Researches, architectures, and constructs production AI vision systems, leveraging state-of-the-art

Detection models and stream processing pipelines to extract attributes and infer intelligence from image and video streams in real time.

Responsibilities:

Model Architecture & Development: Design, train, and fine-tune state-of-the-art vision models (e.g., YOLO variants, DETR, Transformers) for object detection, classification, and attribute extraction.

Stream Processing & Pipeline Engineering: Build low-latency ingestion and processing pipelines for high-throughput video and image streams (e.g., RTSP, WebRTC, frame decoding).

Plate Recognition (LPR/ANPR), Optical Character Recognition (OCR), and multi-object tracking (MOT).

Inference Optimization: Optimize model performance for efficient execution on cloud infrastructure (Kubernetes/GCP) and edge devices using TensorRT, ONNX, or OpenVINO.

Dataset & Active Learning: Curate, annotate, and augment visual datasets, establishing automated model retraining loops and evaluation frameworks.

Requirements:

3+ years of hands-on experience designing and deploying computer vision and deep learning models in production environments.

Deep expertise with modern object detection architectures (YOLO, DETR, Faster R-CNN, Vision Transformers).

Strong programming skills in Python and deep learning frameworks (PyTorch, OpenCV, TensorFlow).

Proven experience working with video stream ingestion, frame processing, and low-latency inference.

Demonstrated experience with License Plate Recognition (LPR), OCR, or fine-grained attribute inference systems.

Familiarity with model quantization, ONNX export, and TensorRT compilation for hardware acceleration.

The Opportunity - Build the Future with Us

The AI Operations & Enablement Analyst ensures support readiness by creating knowledge base content and delivering targeted enablement for the Service Desk and OSS to improve first-contact resolution.

The role also measures and communicates impact by tracking adoption/value metrics, analyzing trends to remove barriers, and producing dashboards and leadership readouts with clear recommendations.

Your Impact

You will:

• Maintain a governed, searchable library of approved playbooks and prompts (version control, change notes, and deprecation of outdated patterns).
• Create and maintain knowledge base articles (how-to, troubleshooting, known issues, escalation paths) to keep the Service Desk and OSS ready to support AI tools and products.
• Deliver targeted enablement for support teams (walkthroughs, office hours, and release briefings) to reduce time-to-resolution and improve first-contact support.
• Track and report adoption and value metrics (usage, retention, feature penetration, time saved), including segmentation by persona, region, and business line.
• Analyze trends to identify adoption barriers and opportunities; recommend targeted actions such as new playbooks, training updates, or product feedback.
• Produce dashboards and monthly/quarterly leadership readouts that connect adoption metrics to outcomes, risks, and recommended next steps.

The Skills That Set You Apart

You bring:

• Demonstrated success working in a matrixed environment, aligning priorities across teams to execute shared goals.
• Experience developing and maintaining reusable AI playbooks and prompt patterns, including version control, change notes, and retirement of outdated content.
• Working knowledge of responsible AI and safe-use practices (privacy, confidentiality, data handling) and the ability to embed quality and verification guardrails into enablement materials.
• Strong technical writing skills with experience producing knowledge base content (how-to, troubleshooting, known issues, escalation paths) and applying content standards/taxonomy to keep information easy to find.
• Proven ability to enable support teams with a focus on improving first-contact resolution and time-to-resolution.
• Strong analytical skills with experience tracking adoption and value metrics (usage, retention, feature penetration, time saved) and segmenting insights by persona, region, and business line.
• Experience producing dashboards and leadership readouts that connect metrics to outcomes, risks, and clear recommendations.
• Ability to identify adoption barriers, synthesize root causes, and drive improvements through updated playbooks, targeted enablement, and actionable product feedback.

#LI-VVB

#Chile

Title: AI Operations & Enablement Analyst

Location: CHL-SANTIAGO

Other Locations: CO-CU-Bogotá D.C., AR-M-Mendoza, MX-MX-Ciudad de México, PE