What You’ll Work On 1. Deep Learning & Computer Vision Train models for image classification: binary/multi-class using CNNs, EfficientNet, or custom backbones. Implement object detection using YOLOv5, Faster R-CNN, SSD; tune NMS and anchor boxes for medical contexts. Work with semantic segmentation models (UNet, DeepLabV3+) for region-level diagnostics (e.g., cell, lesion, or nucleus boundaries). Apply instance segmentation (e.g., Mask R-CNN) for microscopy image cell separation. Use super-resolution and denoising networks (SRCNN, Real-ESRGAN) to enhance low-quality inputs. Develop temporal comparison pipelines for changes across image sequences (e.g., disease progression). Leverage data augmentation libraries (Albumentations, imgaug) for low-data domains. 2. Vision-Language Models (VLMs) Fine-tune CLIP, BLIP, LLaVA, GPT-4V to generate explanations, labels, or descriptions from images. Build image captioning models (Show-Attend-Tell, Transformer-based) using paired datasets. Train or use VQA pipelines for image-question-answer triples. Align text and image embeddings with contrastive loss (InfoNCE), cosine similarity, or projection heads. Design prompt-based pipelines for zero-shot visual understanding. Evaluate using metrics like BLEU, CIDEr, SPICE, Recall@K, etc. 3. Model Training, Evaluation & Interpretation Use PyTorch (core), with support from HuggingFace, torchvision, timm, Lightning. Track model performance with TensorBoard, Weights & Biases, MLflow. Implement cross-validation, early stopping, LR schedulers, warm restarts. Visualize model internals using GradCAM, SHAP, Attention rollout, etc. Evaluate metrics: • Classification: Accuracy, ROC-AUC, F1 • Segmentation: IoU, Dice Coefficient • Detection: mAP • Captioning/VQA: BLEU, METEOR 4. Optimization & Deployment Convert models to ONNX, TorchScript, or TFLite for portable inference. Apply quantization-aware training, post-training quantization, and pruning. Optimize for low-power inference using TensorRT or OpenVINO. Build multi-threaded or asynchronous pipelines for batched inference. 5. Edge & Real-Time Systems Deploy models on Jetson Nano/Xavier, Coral TPU. Handle real-time camera inputs using OpenCV, GStreamer and apply streaming inference. Handle multiple camera/image feeds for simultaneous diagnostics. 6. Regulatory-Ready AI Development Maintain model lineage, performance logs, and validation trails for 21 CFR Part 11 and ISO 13485 readiness. Contribute to validation reports, IQ/OQ/PQ, and reproducibility documentation. Write SOPs and datasheets to support clinical validation of AI components. 7. DevOps, CI/CD & MLOps Use Azure Boards + DevOps Pipelines (YAML) to: Track sprints • Assign tasks • Maintain epics & user stories • Trigger auto-validation pipelines (lint, unit tests, inference validation) on code push • Integrate MLflow or custom logs for model lifecycle tracking. • Use GitHub Actions for cross-platform model validation across environments. 8. Bonus Skills (Preferred but Not Mandatory) Experience in microscopy or pathology data (TIFF, NDPI, DICOM formats). Knowledge of OCR + CV hybrid pipelines for slide/dataset annotation. Experience with streamlit, Gradio, or Flask for AI UX prototyping. Understanding of active learning or semi-supervised learning in low-label settings. Exposure to research publishing, IP filing, or open-source contributions. 9. Required Background 4–6 years in applied deep learning (post academia) Strong foundation in: Python + PyTorch CV workflows (classification, detection, segmentation) Transformer architectures & attention VLMs or multimodal learning Bachelor’s or Master’s degree in CS, AI, EE, Biomedical Engg, or related field 10. How to Apply Send the following to info@sciverse.co.in Subject: Application – AI Research Engineer (4–8 Yrs, CV + VLM) Include: • Your updated CV • GitHub / Portfolio • Short write-up on a model or pipeline you built and why you’re proud of it OR apply directly via LinkedIn — but email applications get faster visibility. Let’s build AI that sees, understands, and impacts lives.
As a Deep Learning and Computer Vision Engineer at Sciverse Solutions Pvt. Ltd., you will be an integral part of the core AI team responsible for designing, training, and deploying models for various applications in the field of microscopy image analysis, diagnostic automation, and embedded vision systems. Your contribution will also involve exploring vision-language models (VLMs) to enhance structured report generation capabilities. Your primary responsibilities will include developing and training deep learning models for tasks such as object detection, segmentation, classification, and metric learning using real-world biomedical and diagnostic datasets. You will work on optimizing models for embedded and edge inference, collaborating with firmware and software teams for seamless deployment, and building scalable vision pipelines for tasks like cell counting and morphology estimation. Additionally, you will have the opportunity to experiment with transformer-based models and VLMs for microscopy captioning and report generation. Collaboration with cross-functional teams, staying updated with the latest advancements in vision and multimodal AI, and possessing a strong foundation in deep learning are essential for success in this role. Must-have skills for this position include a strong understanding of deep learning concepts, experience with image classification and segmentation, proficiency in PyTorch and optionally TensorFlow, and hands-on experience with model deployment and optimization tools. Strong debugging and visualization skills, familiarity with medical imaging formats, and exposure to MLOps tools are considered advantageous. The ideal candidate for this role is a self-starter with a strong sense of ownership, passionate about leveraging AI to solve real-world healthcare challenges, and thrives in an agile, research-to-product environment. By joining Sciverse, you will have the opportunity to work on mission-driven healthcare technology, solve unique AI and vision challenges, and be part of a fast-growing, engineering-driven team. If you are excited about making a meaningful impact in the healthcare industry through cutting-edge technology, we encourage you to apply by emailing your CV, portfolio (if any), and a brief statement outlining your enthusiasm for this role to info@sciverse.co.in.,
Sciverse is searching for an experienced Django Developer who has the capability to work on backend API development and contribute to deep learning and computer vision projects. If you are enthusiastic about creating AI-driven products for diagnostics and microscopy, this position offers an exciting opportunity. Your responsibilities will include designing and developing RESTful APIs using Django and Django REST Framework, collaborating with AI/ML engineers to deploy deep learning models into production, supporting image processing pipelines and handling microscopy data, ensuring secure, scalable, and maintainable backend systems, and optimizing the integration of AI models with backend services. To excel in this role, you should possess at least 5-6 years of professional experience in Django backend development, proficiency in Python, Django REST Framework, and databases such as PostgreSQL/MySQL. Hands-on experience with deep learning frameworks like TensorFlow/PyTorch, familiarity with OpenCV, image processing, and model deployment, as well as a good grasp of API design, Docker, and basic DevOps practices are essential. Additionally, experience with frontend integration (React, Angular), exposure to healthcare, diagnostics, or scientific image analysis projects, and familiarity with cloud platforms such as AWS, Azure, or GCP would be advantageous. At Sciverse, you will have the opportunity to be at the forefront of shaping the future of diagnostics with intelligent, AI-powered solutions that cater to real-world medical and scientific requirements. You will be part of a dynamic and impactful team, driving innovation and making a difference.,