▷ [Apply in 3 Minutes] Manager - ML CV Ops Engineer

3 weeks ago

Bengaluru, India Genpact Full time

Job Description

Inviting applications for the role of Manager, ML CV Ops Engineer

We are seeking a highly skilled ML CV Ops Engineer to join our AI Engineering team. This role is focused on operationalizing Computer Vision models-ensuring they are efficiently trained, deployed, monitored, and retrained across scalable infrastructure or edge environments. The ideal candidate has deep technical knowledge of ML infrastructure, DevOps practices, and hands-on experience with CV pipelines in production. You'll work closely with data scientists, DevOps, and software engineers to ensure computer vision models are robust, secure, and production-ready always.

Key Responsibilities:

End-to-End Pipeline Automation: Build and maintain ML pipelines for computer vision tasks (data ingestion, preprocessing, model training, evaluation, inference). Use tools like MLflow, Kubeflow, DVC, and Airflow to automate workflows.

Model Deployment & Serving: Package and deploy CV models using Docker and orchestration platforms like Kubernetes. Use model-serving frameworks (TensorFlow Serving, TorchServe, Triton Inference Server) to enable real-time and batch inference.

Monitoring & Observability: Set up model monitoring to detect drift, latency spikes, and performance degradation. Integrate custom metrics and dashboards using Prometheus, Grafana, and similar tools.

Model Optimization: Convert and optimize models using ONNX, TensorRT, or OpenVINO for performance and edge deployment. Implement quantization, pruning, and benchmarking pipelines.

Edge AI Enablement (Optional but Valuable): Deploy models on edge devices (e.g., NVIDIA Jetson, Coral, Raspberry Pi) and manage updates and logs remotely.

Collaboration & Support: Partner with Data Scientists to productionize experiments and guide model selection based on deployment constraints.Work with DevOps to integrate ML models into CI/CD pipelines and cloud-native architecture.

Required Qualifications:

- Bachelor's or Master's in Computer Science, Engineering, or a related field.

- Sound experience in ML engineering, with significant work in computer vision and model operations.

- Strong coding skills in Python and familiarity with scripting for automation.

- Hands-on experience with PyTorch, TensorFlow, OpenCV, and model lifecycle tools like MLflow, DVC, or SageMaker.

- Solid understanding of containerization and orchestration (Docker, Kubernetes).

- Experience with cloud services (AWS/GCP/Azure) for model deployment and storage.

Preferred Qualifications:

- Experience with real-time video analytics or image-based inference systems.

- Knowledge of MLOps best practices (model registries, lineage, versioning).

- Familiarity with edge AI deployment and acceleration toolkits (e.g., TensorRT, DeepStream).

- Exposure to CI/CD pipelines and modern DevOps tooling (Jenkins, GitLab CI, ArgoCD).

- Contributions to open-source ML/CV tooling or experience with labeling workflows (CVAT, Label Studio).

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