Description

We are looking for an experienced Lead ML Engineer with solid production expertise in Computer Vision, Statistical Machine Learning, and MLOps. The role involves designing and deploying scalable ML systems while owning the complete ML lifecycle from training and evaluation to deployment and monitoring. The candidate will lead ML engineering initiatives, mentor team members, and collaborate with data scientists to build production-ready solutions.

Strong expertise in Python, PyTorch, Computer Vision, MLOps, and cloud-based ML infrastructure is required.

Responsibilities

- Lead the design and delivery of production-grade ML systems focused on Computer Vision and Statistical Machine Learning use cases.
- Own the end-to-end MLOps lifecycle, including model training, evaluation, versioning, deployment, monitoring, and optimisation.
- Build scalable and modular ML pipelines using tools such as MLflow, Kubeflow, ZenML, or Metaflow.
- Develop and deploy Computer Vision models for detection, segmentation, classification, and multimodal use cases.
- Apply statistical ML techniques including XGBoost, LightGBM, CatBoost, forecasting, Bayesian methods, time series, and causal inference.
- Deploy and serve ML models using REST/gRPC, Triton, BentoML, Ray Serve, or TorchServe with appropriate monitoring and alerting.
- Design and optimise GPU-based training infrastructure using PyTorch DDP/FSDP, Docker, Kubernetes, and cloud GPU services.
- Lead technical discussions, architecture reviews, code reviews, and mentor ML engineers while ensuring engineering and MLOps best practices.

Requirements

- 8+ years of experience in ML Engineering,



with at least 3 years in a Lead/Senior individual contributor capacity.
- Strong hands-on experience building and deploying Computer Vision models using frameworks such as YOLO, Detectron2, RT-DETR, SAM, Mask R-CNN, SegFormer, ViT, DINO/DINOv2, or CLIP.
- Strong knowledge of Statistical Machine Learning, including gradient boosting, forecasting, Bayesian methods, experimental design, feature engineering, time series, and causal inference.
- Deep experience with MLOps, including ML pipelines, experiment tracking, model registries, CI/CD, model monitoring, drift detection, and automated deployment.
- Proficiency in Python, PyTorch, torchvision, HuggingFace Transformers/Timm, OpenCV, scikit-learn, and XGBoost.
- Experience with Docker, Kubernetes, model serving frameworks such as Triton/BentoML, and cloud platforms including AWS, GCP, or Azure.
- Hands-on experience with GPU infrastructure and distributed training, including PyTorch DDP/FSDP, mixed precision, and training performance optimisation.
- Strong software engineering fundamentals with experience in system design, clean code, testing, Git, code reviews, and technical leadership.

Key focus areas:

- Strong production experience in Computer Vision and Statistical Machine Learning
- Hands-on expertise with Python, PyTorch, OpenCV and HuggingFace
- Experience with CV models such as YOLO, Detectron2, RT-DETR, SAM, ViT, DINO/DINOv2 or CLIP
- Strong experience in MLOps, MLflow/Kubeflow, model deployment, monitoring and CI/CD
- Hands-on experience with Docker, Kubernetes and model serving frameworks
- Experience with GPU infrastructure and distributed training using PyTorch DDP/FSDP
- Strong software engineering, system design and technical leadership experience

📌 Lead Engineer / Associate Architect - ML Engineer (Bengaluru)
🏢 QBurst
📍 Bengaluru