Job Summary
Rules and Responsibilities
Job Description
Senior Manager (AI/ML) will serve as the technical and strategic lead for the AI Analytics stream of the CCTV Camera Video Analytics project. This position requires a seasoned AI Model Deployment Engineer professional with expertise in deploying deep learning models, managing AI pipelines, optimizing performance on edge and server infrastructure, and leading a multidisciplinary team of AI engineers. The role will also include technical oversight, team coordination, architecture design, and integration of AI analytics with the Video Management System (VMS).
As the Lead for AI Analytics, the candidate will collaborate closely with the VMS Integration Lead and the Project Manager to ensure that AI capabilities are effectively integrated, tested, deployed, and optimized across multiple camera streams and environments. The Lead is expected to establish robust CI/CD pipelines for AI models, ensure scalability, manage hardware constraints, and guide model retraining or fine-tuning processes when necessary.
Job Responsibilities
- Lead the AI analytics team including Computer Vision Engineers and QA testers.
- Design and implement efficient deployment pipelines for multiple AI models (face recognition, object detection, behavior recognition, etc.) across 12+ CCTV streams.
- Architect containerized environments (Docker, Kubernetes, etc.) for scalable AI model deployment.
- Optimize inference performance on high-end GPU workstations (e.g., NVIDIA RTX 4090) and edge devices (Jetson, Xavier, etc.).
- Monitor, test, and debug deployed models using tools like Prometheus, Grafana, or custom dashboards.
- Collaborate with the VMS integration team to ensure seamless flow of processed video streams and inference results into the central system.
- Develop robust APIs or use frameworks such as GStreamer, FastAPI, or NVIDIA DeepStream for real-time integration.
- Conduct stress and performance testing to maintain consistent operation during peak load.
- Coordinate with data engineers (merged with CV team) for preprocessing, labeling, and versioning of datasets.
- Ensure all deployments are compliant with security, privacy, and access control protocols.
- Maintain documentation of deployment procedures, architecture diagrams, and version control of models.
Required Skills
Skills and Qualifications
Qualification
- Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline.
Experience
- 3+ years of experience in AI model deployment or ML Ops.
- At least 1 year in a leadership or senior engineer capacity.
- Demonstrated experience deploying computer vision models in production environments (preferably for surveillance or security domains).
- Experience in real-time video processing using OpenCV, TensorRT, DeepStream, or similar frameworks.
Certifications (Preferred):
· NVIDIA Certified Deep Learning Institute credentials
· Google Cloud/AWS ML Engineer certifications
· Docker & Kubernetes certifications
· ML Ops or DevOps relevant certificates
Skills
- Expertise in containerization and orchestration (Docker, Docker Compose, Kubernetes).
- Proficiency with Python, Bash scripting, and AI frameworks (TensorFlow, PyTorch, ONNX).
- Deep understanding of GPU optimization, memory profiling, and inference acceleration.
- Familiarity with deployment tools (TorchServe, Triton Inference Server, etc.).
- Experience with CI/CD pipelines for AI workflows (GitLab CI, Jenkins, etc.).
- Knowledge of GStreamer, RTSP, and handling real-time video feeds.
Desired Personal Qualities:
- Proven leadership and team mentoring capabilities.
- Analytical and solution-oriented mindset with strong troubleshooting skills.
- Ability to manage multiple priorities in a fast-paced environment.
- Excellent communication skills to interface with both technical and non-technical stakeholders.
- Ownership-driven with a commitment to delivery excellence.
Additional Notes
- Candidates who applied earlier for the same position may not apply again. Their application submitted earlier will be included in the selection process.
