Location: Bangalore
No. of Positions: 01
Experience: 2-4yrs
Role Description
We are looking for a Machine Learning & Computer Vision Engineer to design, develop, and deploy end-to-end AI solutions. The role involves building deep learning and classical ML models, creating scalable ML pipelines, and delivering production-ready systems on AWS. You will work on object detection, segmentation, OCR, tracking, and other vision tasks using modern frameworks, while applying strong MLOps practices for automation, monitoring, and continuous improvement. Collaboration with Data Engineering, Product, and Backend teams is essential to integrate AI capabilities into enterprise applications and ensure high performance and reliability.
Key Responsibilities:
- Design and develop machine learning and computer vision models, including deep learning and classical ML solutions.
- Implement architectures for object detection, segmentation, tracking, OCR, anomaly detection, and other vision tasks.
- Build end-to-end ML pipelines encompassing data acquisition, preprocessing, feature engineering, model training, validation, optimization, and production deployment.
- Leverage classical machine learning algorithms such as Random Forests, Gradient Boosting (XGBoost / LightGBM / CatBoost), SVM, Linear/Logistic Regression, KNN, PCA, Clustering, etc., when appropriate for tabular or image-feature-based problems.
- Conduct model experimentation and performance evaluation using relevant metrics and statistical techniques.
- Deploy ML models to production using AWS services such as SageMaker, Lambda, S3, EC2, ECR, Step Functions, API Gateway, DynamoDB, CloudWatch, and EKS.
- Build and manage scalable real-time or batch inference systems.
- Collaborate with Data Engineering, Product, and Backend teams to integrate AI components into enterprise solutions.
- Apply MLOps practices including CI/CD, model versioning, automated retraining, monitoring, and observability.
- Work with large-scale image/video/audio datasets and manage annotation and data processing workflows.
Required Qualifications
- 2-4 years of hands-on experience in machine learning, including both classical ML and deep learning.
- Strong experience with deep learning frameworks: PyTorch, TensorFlow, Keras.
- Solid grounding in classical ML, feature engineering, model selection, hyperparameter tuning, and evaluation techniques.
- Expertise in computer vision frameworks such as OpenCV, Detectron2, YOLO, Mask R-CNN, UNet, ViT, OpenVINO, etc.
- Strong ability to write production-grade code in Python, including NumPy, Pandas, Scikit-learn, and model-serving frameworks (FastAPI / Flask).
- Proven experience deploying solutions on the AWS cloud ecosystem.
- Experience with Docker, Kubernetes, and distributed or accelerated training environments.
- Hands-on experience with REST APIs, microservices architecture, and real-time inference.
- Familiarity with MLOps tools and practices such as MLflow, Kubeflow, Airflow, or SageMaker Pipelines.
- Strong understanding of software engineering fundamentals: Git, testing, system performance, and release management.
Preferred Skills
- Exposure to Generative AI / multimodal models / video analytics.
- Experience deploying on edge devices (NVIDIA Jetson, TensorRT, ONNX Runtime).
- Experience with data labeling tools and annotation pipelines.
- Familiarity with CI/CD frameworks (GitLab, GitHub Actions, AWS CodePipeline).
- Experience with Agile/Scrum processes.
