Join Our Trailblazing Team at Neuramonks!

Key Responsibilities:

- Assist in the development and training of machine learning models, particularly in the realm of large language models (LLMs) and generative AI.

- Conduct research on current trends and advancements in AI/ML technologies.

- Support data collection, preprocessing, and analysis to enhance model performance.

- Collaborate with the team to design experiments and evaluate model outcomes.

- Implement fine-tuning techniques on pre-trained models to improve their task-specific performance.

- Work with text representation models, including embeddings, n-grams, and sentiment analysis, to enhance the understanding of textual data.

- Leverage cutting-edge models like transformers (e.g., BERT, GPT) for tasks such as text generation, classification, and language understanding.

- Develop and work with agentic frameworks to create autonomous agents and intelligent systems capable of task execution and decision-making.

- Explore and experiment with new and upcoming frontiers in the AI/ML field, pushing the boundaries of current technologies.

- Document findings and contribute to project reports and presentations

- Participate in team meetings and contribute ideas for AI-driven projects.

Required Skills:

- Basic Knowledge of AI/ML: Understanding of fundamental concepts in machine learning and artificial intelligence.

- Programming Skills: Proficiency in Python; familiarity with libraries such as TensorFlow, PyTorch, Hugging Face, and scikit-learn is a plus.

- Data Handling: Experience with data manipulation and analysis using tools like Pandas, NumPy, and similar data processing libraries.

- Mathematical Proficiency: Understanding of linear algebra, calculus, and statistics as they relate to machine learning and AI.

- Research Skills: Ability to conduct literature reviews and synthesize information on emerging AI technologies, particularly in LLMs and generative models.

- Text Representation Models: Experience with word embeddings, n-grams, sentiment analysis, and utilizing transformers (BERT, GPT, etc.)

- Agentic Frameworks: Expertise in working with autonomous agents, intelligent systems, and agent-based modeling frameworks.

- Problem-solving abilities: Strong analytical and critical thinking skills.

- Team Collaboration: Good communication skills and the ability to work effectively in a team environment.

- Eagerness to Learn: A passion for AI/ML and a desire to stay updated with the latest industry trends and developments, especially in frontier areas of AI research.

Key Responsibilities:

- Assist in the development, training, and optimization of computer vision models for various applications, including object detection, segmentation, classification, and image generation.

- Conduct research on the latest advancements in computer vision, deep learning, and image processing techniques.

- Work with image and video datasets, performing data preprocessing, augmentation, and analysis to enhance model performance.

- Implement and fine-tune state-of-the-art deep learning architectures such as CNNs (ResNet, EfficientNet), Vision Transformers (ViTs), and GANs.

- Develop and optimize vision models using frameworks like TensorFlow, PyTorch, and OpenCV.

- Utilize feature extraction, edge detection, and other classical computer vision techniques to complement deep learning models.

- Explore and implement foundation models in vision, including CLIP, DINO, SAM, and Diffusion Models, for cutting-edge applications.

- Deploy and integrate computer vision models into real-world applications, including edge devices, cloud platforms, and embedded systems.

- Collaborate with cross-functional teams to define use cases, evaluate model performance, and refine approaches for better accuracy and efficiency.

- Document research findings, model performance metrics, and key insights for internal reports and presentations.

- Stay updated with new trends in computer vision, AI, and deep learning research to incorporate innovative solutions.

Skills Required:

- Basic Knowledge of AI/ML:  Understanding of fundamental concepts in machine learning and artificial intelligence.

- Computer Vision Fundamentals: Strong understanding of image processing, feature extraction, and object recognition.

- Programming Skills: Proficiency in Python; experience with libraries such as OpenCV, TensorFlow, PyTorch, and scikit-image is a plus.

- Deep Learning Models: Familiarity with CNNs, Vision Transformers, and generative models for tasks like object detection (YOLO, Faster R-CNN), segmentation (UNet, SAM), and image synthesis (GANs, Diffusion Models).

- Data Handling & Preprocessing: Experience in handling large-scale image/video datasets using NumPy, Pandas, and OpenCV for data augmentation and transformation.

- Mathematical Proficiency: Understanding of linear algebra, probability, and optimization techniques relevant to computer vision and AI.

- Model Optimization: Experience in techniques such as pruning, quantization, and knowledge distillation to enhance model efficiency.

- Deployment & Integration: Understanding of model deployment on cloud (AWS, GCP) and edge devices (TensorRT, OpenVINO, TFLite).

- Problem-Solving Abilities: Strong analytical thinking and debugging skills to handle real-world vision challenges.

- Team Collaboration: Good communication skills and the ability to work effectively in a team environment.

- Eagerness to Learn: A passion for AI, deep learning, and computer vision, with a drive to stay updated with the latest research and industry advancements.