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Urgent! Blue Cloud Softech - AI Developer - Deep Learning Job Opening In Bengaluru – Now Hiring Blue Cloud Softech Solutions Limited

Blue Cloud Softech AI Developer Deep Learning



Job description

<p><p><b>About the job :</b></p><p><br/><b>About the Role :</b></p><p><br/></p><p>Were seeking an AI Developer with strong expertise in deep learning (CNNs, RNNs, Transformers) and hands-on experience in computer vision and sequence modeling.

You will drive the development of AI systems that integrate perception (vision models), reasoning (LLMs), and action (multi-agent orchestration).

This role requires both research depth and production engineering rigor, with end-to-end ownership of training, scaling, deployment, and monitoring of AI systems.<br/><br/><b>Key Responsibilities Learning (Primary Focus) : </b></p><p><br/></p><p>- Architect and train CNN/ViT models for classification, detection, segmentation, and OCR.</p><p><br/></p>- Build and optimize RNN/LSTM/GRU models for sequence learning, speech, or timeseries forecasting.<br/><br/></p><p>- Research and implement transformer-based architectures bridging vision and language tasks.<br/><br/></p><p>- Create scalable pipelines for data ingestion, annotation, augmentation, and synthetic data generation.<br/><br/><p><b>Agentic AI & Multi-Agent Frameworks :</b></p><p><br/></p><p>- Design and implement multi-agent workflows using LangChain, LangGraph, CrewAI, or similar </p><p>frameworks.</p><p><br/></p>- Develop role hierarchies, state graphs, and integrations that enable autonomous vision + </p><p>language workflows.<br/><br/></p><p>- Optimize agent systems for latency, cost, and reliability.<br/><br/><p><b>LLM Fine-Tuning & Retrieval-Augmented Generation (RAG) :</b></p><p><br/></p><p>- Fine-tune open-weight LLMs using LoRA/QLoRA, PEFT, or RLHF methods.</p><p><br/></p>- Develop RAG pipelines integrating vector databases (FAISS, Weaviate, pgvector).<br/><br/></p><p>- Combine LLM reasoning with CNN/RNN perception modules in multimodal systems.<br/><br/><p><b>MLOps & Deployment at Scale :</b></p><p><br/></p><p>- Develop reproducible training workflows with PyTorch/TensorFlow and experiment tracking </p><p>(W&B, MLflow).</p><p><br/></p>- Deploy models with TorchServe, Triton, or KServe on cloud AI stacks (AWS Sagemaker, GCP </p><p>Vertex, Kubernetes).<br/><br/></p><p>- Optimize inference with ONNX/TensorRT, quantization, and pruning for cloud and edge devices.<br/><br/></p><p>- Build robust APIs/micro-services (FastAPI, gRPC) and ensure CI/CD, monitoring and </p><p>automated retraining.<br/><br/><p><b>Collaboration & Mentorship :</b></p><p><br/></p><p>- Translate business needs into scalable deep learning solutions.</p><p><br/></p>- Mentor junior engineers in CNNs, RNNs, and production ML practices.<br/><br/></p><p>- Lead technical reviews and promote best practices across the team.<br/><br/><b>Minimum Qualifications :</b></p><p><br/></p><p>- B.S./M.S. in Computer Science, or related discipline.<br/><br/></p><p>- 5+ years building deep learning systems with CNNs and RNNs in production.<br/><br/></p><p>- Strong Python skills and Git workflows.<br/><br/></p><p>- Proven delivery of computer vision pipelines (OCR, classification, detection).<br/><br/></p><p>- Hands-on experience with LLM fine-tuning and multimodal AI.<br/><br/></p><p>- Experience in containerization (Docker) and deployment on cloud AI platforms.<br/><br/></p><p>- Knowledge of distributed training, GPU acceleration, and inference optimization.<br/><br/><b>Preferred Qualifications :</b></p><p><br/></p><p>- Research experience in transformer architectures (ViTs, hybrid CNN-RNN Transformer </p><p>models).</p><p><br/></p><p>- Prior work in sequence modeling for speech or time-series data.<br/><br/></p><p>- Contributions to open-source deep learning frameworks or vision/sequence datasets.<br/><br/></p><p>- Experience with edge AI deployment<br/></p><br/></p> (ref:hirist.tech)


Required Skill Profession

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