Job Description

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