Find Your Dream Deep Learning Job in India

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.

Associate Architect – Deep learning

Skills:

• R, Python, C++
• Statistical / Machine Learning techniques
• Good to have deep learning framework like TensorFlow, Keras, PyTorch
• Excellent presentation and customer interaction skills.

Job Description:

The key role of this profile is to work with customers directly and make them aware of technics available in DL and make best use of NVidia h/w & s/w. This includes installation and configuration of Nvidia DGX-family and different software stack (i.e. TRT/DS/usage of Tensorcore/AMP/NGC/Monitoring tools/Debugging tools/CUDA/other library & SDK/SLURM/Kubernetes). The role also includes few levels of project guidance and working jointly with customer on their journey of adopting right deep learning technics (both Training and Edge/Inferencing stack). Key technical skills which will be evaluated during interview is as follow:

• Well worst with one of the Deep learning framework: TensorFlow/PyTorch/MXNet/Caffe
• Should have done one project using Nvidia h/w and DL framework
• Good knowledge on H/w, OS troubleshooting & Shell scripting
• Well understanding on Hardware and willing to learn Data centre implementation along with Deep learning.
• Excellent in presentation and public speaking
• BE/B.Tech. in Engineering, Mathemat