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.
