OnGrid - Senior Artificial Intelligence Engineer - NLP/Generative AI (2-4 yrs)

Job Description :

We are looking for a motivated SeniorAI Engineer with experience in building high-performing, scalable, enterprise-grade applications, to join us, driving this vision, and taking it to a larger scale. If you are a visionary, always on the lookout for the right solutions, and a technology geek, constantly seeking to learn and improve your skill set, then you are the type of person we are looking for.

Responsibilities :

- Collaborate with product and tech teams to manage and innovate AI strategy and R and D.

- Contribute to impactful AI use cases for internal and client applications.

- Develop AI/ML solutions for complex problems, maintaining ethical standards.

- Conduct research in fields like computer vision, NLP, Gen AI, and deep learning.

- Integrate and refine AI models in production systems using live data.

- A Bachelor's or Master's degree in Computer Science or a related field. Specialization in Data Science, Machine Learning, or Artificial Intelligence is a plus.

- A minimum of 3+ years of hands-on experience in solving real-world AI challenges, with a focus on areas such as Computer Vision, Deep Learning, Object Detection, Natural Language Processing (NLP), Generative AI, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and AI agents.

- Strong proficiency in Python programming, with experience in frameworks like Django and Flask;

- Knowledge of Java is advantageous.

- Familiarity with Microservices Architecture is a plus.

- Experience with cloud platforms (AWS, Azure, or Google Cloud) is desirable.

- Vision Models : Experience with vision models such as YOLO, ResNet, and OCR for real-time object detection, image classification, and text detection.

- Generative AI and LLMs : Expertise in generative AI models and large language models (LLMs) such as Claude, LLaMA, Qwen, Mistral, etc., as well as experience with multimodal models and AI-agent frameworks.

- Proficient in implementing Retrieval-Augmented Generation (RAG) and working with open-source models from platforms like Hugging Face, Robo Flow, etc.

- AI Frameworks and Tools : Proficiency in using frameworks and libraries such as TensorFlow, PyTorch, LangChain (LangGraph, LangSmith), PydanticAI, OpenCV, Django, Flask, Postman, Docker, Swagger, AWS, and GCP.

- Accelerated Computing : Strong understanding of GPU-accelerated training, fine-tuning, and inference, with experience on platforms such as AWS, GCP, or in-house infrastructure.

- Production Deployment : Expertise in deploying, fine-tuning, and scaling AI models in production environments, ensuring system reliability and high availability.

- Advanced AI Knowledge : In-depth understanding of convolutional neural networks, transformer architectures, vector databases, and data preparation pipelines.