Job Requirements
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 c
omputer 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.
Work Experience
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.
