Senior AI Engineer

Job Description – Senior AI Engineer

Experience Required: 6+ years

Role Overview

We are seeking an experienced AI Engineer (Level 3) to join our team. The ideal candidate has a strong background in computer vision model training (YOLOv8), cloud-based large-scale training, and production-grade deployment. You will work across the full lifecycle of AI models—from data annotation and preparation to training, testing, deployment, and monitoring in real-world environments.

Key Responsibilities:

Data Preparation & Annotation

• Categorize, annotate, and QA large-scale video datasets using V7 or custom scripts.

Model Training & Evaluation

• Build and fine-tune YOLOv8 (or similar detection models).
• Perform hyperparameter tuning and model optimization.
• Evaluate models with metrics such as mAP, precision, recall, and per-class analysis.

Cloud Training & Scalability

• Train models on AWS, GCP, or Azure (SageMaker, Vertex AI, etc.).
• Optimize GPU/TPU resource usage and handle large datasets efficiently.
• Implement distributed training and cost-performance trade-offs.

Production-Grade Deployment

• Package and deploy models into production with CI/CD pipelines.
• Serve models via APIs and monitor for drift, latency, and accuracy.
• Implement logging, monitoring, and automated retraining pipelines.

Testing & Issue Analysis

• Design systematic test scenarios across diverse environments (lighting, weather, hardware installs).
• Measure detection accuracy, false positives/negatives, and performance KPIs.
• Conduct root cause analysis of failures and propose improvements.

Tooling & Visualization

• Use OpenCV, Sklearn, Voxel51, Kibana, and Power BI for analysis and reporting.
• Generate clear insights and communicate performance to stakeholders.

Required Skills & Experience (6+ years)

• Strong expertise in YOLOv8 training and evaluation.
• Proven experience with cloud-based model training (AWS/GCP/Azure).
• Hands-on experience with production-grade deployment of AI models.
• Proficiency with annotation pipelines, testing frameworks, and performance reporting.
• Strong coding skills in Python and related ML libraries.

Good-to-Have Skills

• MLOps tools: MLflow, Kubeflow.
• Containerization: Docker.
• Orchestration: Kubernetes.
• Edge AI deployment: model optimization for Jetson or similar devices.
• Other CV models: DINOv2, DETR, segmentation models.
• Scripting & Automation: Bash, workflow automation.
• Dashboarding: building real-time monitoring dashboards.