Blue Cloud Softech - Artificial Intelligence Engineer - Deep Learning

Job Description : AI Developer (Deep Learning, Computer Vision & Agentic AI)

Location : Hybrid Bangalore

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 :

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 :

- 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.