Solves complex problems and help stakeholders make data- driven decisions by leveraging quantitative methods such as machine learning It often involves synthesizing large volume of information and extracting signals from data in a programmatic way Roles Responsibilities Deep Learning 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 Desired Candidate 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 Solid 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-RNNTransformer 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 Competencies Values Integrity Accountability Inclusion Innovation Teamwork Job Locations Bangalore Karn taka 560076 India Travel Required 10 to 25 Requisition ID 142959 Workplace Type External Careers Page Information Technology

📌 Sr. Ai Developer (Karnataka)
🏢 TE Connectivity
📍 Karnataka