data scientist

Overview

WE ARE HIRING!

Position: Data Scientist

Location: Bangalore (On-Site)

Experience Level: 3+ years in AI/ML development with a focus on cutting-edge technologies Employment Type: Full-time

Qualifications:-

1)Education: Bachelor’s or Master’s in Computer Science, Data Science, Artificial Intelligence, or related field.

2)Experience: 3+ years in AI/ML development, with hands-on experience in deploying real-world AI solutions.

3)Proven experience with PyTorch, TensorFlow, NLP, OCR, and Transformers (Hugging Face, etc.).

4)Extensive work with NVIDIA GPUs, Jetson AGX/Orin, and optimizing models with TensorRT and ONNX.

5)Familiarity with Vision Transformers (ViTs) and LLM architectures.

6)Experience in deploying AI pipelines on hyperscalers (AWS, Azure, or GCP).

7)Expertise in MLOps tools and frameworks (e.g., MLflow, Kubeflow, or SageMaker).

Skills: -

1)Strong understanding of deep learning algorithms, including transformer architectures and vision models.

2)Proficiency in optimizing inference for edge devices and cloud environments.

3)Solid programming skills in Python and familiarity with C++ is a plus.

4)Familiarity with containerization (Docker) and orchestration (Kubernetes).

Preferred Qualifications :-

1)Experience with distributed training on multi-GPU setups.

2)Contributions to open-source AI/ML projects.

3)Familiarity with NVIDIA SDKs like DeepStream or Isaac for specialized applications.

4)Strong publication record in reputable AI/ML journals or conferences.

Key Responsibilities :-

1. AI/ML Model Development:

1)Develop, fine-tune, and deploy deep learning models using PyTorch and TensorFlow.

2)Work extensively with NLP frameworks, OCR pipelines, Transformers, and Vision Transformers (ViTs) for various use cases.

3)Implement and optimize large language models (LLMs) for specific business requirements.

2.Edge AI and Hardware Integration:

1)Design and deploy AI models on NVIDIA GPUs and NVIDIA edge devices, such as Jetson AGX and Orin.

2)Optimize model performance using TensorRT and integrate workflows with ONNX models for efficient edge deployment.

3.MLOps and Model Deployment:

1)Manage end-to-end machine learning pipelines with MLOps best practices.

2)Automate training, validation, deployment, and monitoring workflows for scalable and reproducible AI solutions.

4.Performance Optimization and Scaling:

1)Optimize model performance on hyperscalers like AWS, Azure, or Google Cloud.

2)Implement distributed training and inference strategies for large-scale datasets.

5.Research and Innovation:

1)Stay up-to-date with the latest advancements in AI, such as cutting-edge transformer models and edge AI solutions.

2)Drive innovation by proposing and implementing new methodologies to solve complex business challenges.

Job Type: Full-time

Work Location: In person