About the job : About the Role :
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.
Key Responsibilities 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 similarframeworks.
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, GCPVertex, 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 andautomated 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, 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 Transformermodels).
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(ref : hirist.tech)