63 Model Optimization Jobs - Page 3

Key Skills: Machine Learning, Data Engineering, Python, TensorFlow, PyTorch, Statistical Analysis, Cloud Platforms (AWS/GCP/Azure), Data Structures, Big Data, ML Frameworks, Model Optimization, Predictive Modeling. Key Responsibilities: Develop and implement data-driven ML predictive models to advance intelligent automation and reporting. Assess, analyze, and organize large datasets to extract meaningful insights. Execute tests to validate machine learning models and algorithms. Optimize machine learning models for improved performance and accuracy. Collaborate with cross-functional teams to integrate machine learning solutions into existing systems. Monitor and maintain machine learning models to ensure ongoing accuracy and efficiency. Research and stay updated on the latest advancements in machine learning and ML techniques and applications. Document processes, methodologies, and model performance for transparency and reproducibility. Troubleshoot and resolve issues related to machine learning model deployment and operation. Mentor and provide technical guidance to junior engineers, sharing best practices and fostering a culture of continuous learning. Experience Requirement: 6-13 years of experience in machine learning, data engineering, or software development. Strong programming skills and proficiency in languages commonly used for ML and related tasks. Big Data skills and proficiency in data structures, modeling, and visualization. Proven background in statistical analysis and data analysis to support model training and evaluation. Excellent communication skills for working with stakeholders and translating technical concepts. Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn); experience with production-quality code. Strong background in statistics, mathematics, and data science. Experience with cloud platforms (AWS, GCP, Azure) and deploying ML models at scale is a plus. Education: Any Post Graduation, Any Graduation.

The ideal candidate for this position in Ahmedabad should be a graduate with at least 3 years of experience. At Bytes Technolab, we strive to create a cutting-edge workplace infrastructure that empowers our employees and clients. Our focus on utilizing the latest technologies enables our development team to deliver high-quality software solutions for a variety of businesses. You will be responsible for leveraging your 3+ years of experience in Machine Learning and Artificial Intelligence to contribute to our projects. Proficiency in Python programming and relevant libraries such as NumPy, Pandas, and scikit-learn is essential. Hands-on experience with frameworks like PyTorch, TensorFlow, Keras, Facenet, and OpenCV will be key in your role. Your role will involve working with GPU acceleration for deep learning model development using CUDA, cuDNN. A strong understanding of neural networks, computer vision, and other AI technologies will be crucial. Experience with Large Language Models (LLMs) like GPT, BERT, LLaMA, and familiarity with frameworks such as LangChain, AutoGPT, and BabyAGI are preferred. You should be able to translate business requirements into ML/AI solutions and deploy models on cloud platforms like AWS SageMaker, Azure ML, and Google AI Platform. Proficiency in ETL pipelines, data preprocessing, and feature engineering is required, along with experience in MLOps tools like MLflow, Kubeflow, or TensorFlow Extended (TFX). Expertise in optimizing ML/AI models for performance and scalability across different hardware architectures is necessary. Knowledge of Natural Language Processing (NLP), Reinforcement Learning, and data versioning tools like DVC or Delta Lake is a plus. Skills in containerization tools like Docker and orchestration tools like Kubernetes will be beneficial for scalable deployments. You should have experience in model evaluation, A/B testing, and establishing continuous training pipelines. Working in Agile/Scrum environments with cross-functional teams, understanding ethical AI principles, model fairness, and bias mitigation techniques are important. Familiarity with CI/CD pipelines for machine learning workflows and the ability to communicate complex concepts to technical and non-technical stakeholders will be valuable.,

You are an experienced Lead Generative AI Engineer responsible for training, optimizing, scaling, and deploying various generative AI models including large language models, voice/speech foundation models, vision, and multi-modal foundation models using cutting-edge techniques and frameworks. Your role involves architecting and implementing state-of-the-art neural architecture, robust training, and inference infrastructure to efficiently take complex models with billions of parameters to production while optimizing for low latency, high throughput, and cost efficiency. Your key responsibilities include: - Architecting and refining foundation model infrastructure to support optimized AI models deployment focusing on C/C++, CUDA, and kernel-level programming enhancements. - Implementing optimization techniques like quantization, distillation, sparsity, streaming, and caching for model performance enhancements. - Spearheading the development of Vision pipelines to ensure scalable training and inference workflows of billions of parameter foundation models. - Innovating for state-of-the-art architectures involving Panoptic Segmentation, Image Classification, and Image Generation. - Designing, developing, and innovating state-of-the-art large multimodal models. - Executing training and inference processes to minimize latency and maximize throughput utilizing GPU clusters and custom hardware. - Integrating and tailoring frameworks like PyTorch, TensorFlow, DeepSpeed, Lightening, FSDP, and Habana for fast model training and inference. - Enhancing post-deployment mechanisms with exhaustive testing, real-time monitoring, and robustness checks. - Driving continuous improvement initiatives for deployed models with automated pipelines for drift detection and performance degradation. - Leading the charge in model management including version control, reproducibility, and lineage tracking. - Cultivating a culture of high-performance computing and optimization within the AI/ML domain. Qualifications: - Ph.D. with 5+ years or MS with 8+ years of experience in ML Engineering, Data Science, or related fields. - Demonstrated expertise in high-performance computing with proficiency in Python, C/C++, CUDA, and kernel-level programming for AI applications. - Extensive experience in optimizing training and inference for large-scale AI models. - Understanding of Diffusion Models, Variational Autoencoders, Bayesian Modelling, and Reinforcement Learning is beneficial. - Experience in building billions of parameters generative AI foundation models. - Proven success in deploying optimized ML systems on a large scale using cloud infrastructures and GPU resources. - In-depth understanding and hands-on experience with advanced model optimization frameworks and MLOps tools. - Familiarity with contemporary MLOps frameworks and their application in production environments. - Strong grasp of state-of-the-art ML infrastructures, deployment strategies, and optimization methodologies. - Innovative problem-solving skills and collaborative mindset. - Exceptional communication and team collaboration skills.,

National Payment Corporation of India (NPCI) is hiring Data Scientist (with Deep Learning Experience) Job Title: Data Scientist (Deep Learning Strong experience) Experience: 2 Years to 8 Years MUST HAVE SKILLS REQUIRED: (STRONG) Programming & DH : Python, Data Structures, Algorithms (STRONG) Data Handling : Python, EDA, Statistics, Probability Distributions (STRONG) Learning & Deep Learning : TensorFlow, PyTorch, GANs, Transformers (STRONG) Cloud & GPU: GCP, GPU-based machines, Model Optimization (STRONG) Model Lifecycle : Custom Loss Functions, Evaluation Metrics, Scalability If you find yourself Perfectly fit based on above skillset criteria. Please share your Updated resume at vinayak.ojha_ori@npci.org.in Overview: NPCI Data Analytics team is seeking a talented and motivated Data Scientist to join our team. As a Data Scientist, you will play a crucial role in modelling complex problems, uncovering insights, and identifying new opportunities through the use of statistical, algorithmic, mining, and visualization techniques. You will be focused on utilizing NPCI product data and analytics to solve business problems by applying machine learning and deep learning techniques, conducting statistical analysis, and building high-quality predictive systems that are integrated with our services. Key Responsibilities: Develop and implement deep learning models for various applications. Work with large datasets and perform exploratory data analysis. Develop custom loss functions and evaluation metrics. Collaborate with cross-functional teams to ensure successful deployment of models. Optimize models for performance, scalability, and efficiency. Conduct research and stay up to date with the latest developments in the field of deep learning. Participate in code reviews and provide feedback to team members. Qualifications: Bachelor's or Master's degree in Computer Science, Mathematics, or related field with 3+ years of experience. Strong proficiency in Python programming language. Good understanding of data structures and algorithms. Strong understanding of statistics and probability distributions Hands-on Experience with GANs and Transformers Experience with both Tensorflow and Pytorch frameworks. Familiarity with Google Cloud Platform (GCP) and working on GPU based machines. Strong problem-solving skills. Excellent written and verbal communication skills. Ability to work independently as well as in a team environment. Strong Communication skills and ability to simplify complex information and results into simple concepts and actionable insights for a variety of audiences.

Description We are seeking a skilled Computer Vision Engineer to join our team in India. The ideal candidate will have extensive experience in developing and implementing computer vision solutions, contributing to innovative projects that leverage advanced imaging technologies. Responsibilities Develop and implement computer vision algorithms for various applications. Collaborate with cross-functional teams to integrate computer vision solutions into products. Conduct research to improve existing computer vision models and frameworks. Analyze and preprocess image and video data for training machine learning models. Optimize computer vision algorithms for performance and efficiency. Stay updated with the latest trends and advancements in computer vision technologies. Skills and Qualifications 6-12 years of experience in computer vision or related fields. Strong proficiency in programming languages such as Python, C++, or Java. Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras. Solid understanding of machine learning and statistical modeling techniques. Knowledge of image processing libraries like OpenCV, PIL, or scikit-image. Experience with computer vision tasks such as object detection, image segmentation, and facial recognition. Familiarity with cloud platforms and GPU programming for scalable model training. Strong analytical and problem-solving skills.

Position Overview : We are seeking an experienced NLP & LLM Specialist to join our team. The ideal candidate will have deep expertise in working with transformer-based models, including GPT, BERT, T5,RoBERTa, and similar models. This role requires experience in fine-tuning these pre-trained models on domain-specific tasks, as well as crafting and optimizing prompts for natural language processing tasks such as text generation, summarization, question answering, classification, and translation. The candidate should be proficient in Python and familiar with NLP libraries like Hugging Face, SpaCy, and NLTK, with a solid understanding of model evaluation metrics. Roles and Responsibilities : Model Expertise : Work with transformer models such as GPT, BERT, T5, RoBERTa, and others for a variety of NLP tasks, including text generation, summarization, classification, and translation. Model Fine-Tuning: Fine-tune pre-trained models on domain-specific datasets to improve performance for specific applications such as summarization, text generation, and question answering. Prompt Engineering: Craft clear, concise, and contextually relevant prompts to guide transformer-based models towards generating desired outputs for specific tasks. Iterate on prompts to optimize model performance. Instruction-Based Prompting: Implement instruction-based prompting to guide the model toward achieving specific goals, ensuring that the outputs are contextually accurate and aligned with task objectives. Zero-shot, Few-shot, Many-shot Learning: Utilize zero-shot, few-shot, and many-shot learning techniques to improve model performance without the need for full retraining. Chain-of-Thought (CoT) Prompting: Implement Chain-of-Thought (CoT) prompting to guide models through complex reasoning tasks, ensuring that the outputs are logically structured and provide step-by-step explanations. Model Evaluation: Use evaluation metrics such as BLEU, ROUGE, and other relevant metrics to assess and improve the performance of models for various NLP tasks. Model Deployment: Support the deployment of trained models into production environments and integrate them into existing systems for real-time applications. Bias Awareness: Be aware of and mitigate issues related to bias, hallucinations, and knowledge cutoffs in LLMs, ensuring high-quality and reliable outputs. Collaboration: Collaborate with cross-functional teams including engineers, data scientists, and product managers to deliver efficient and scalable NLP solutions. Must Have Skill : Overall 7 years with at least 5+ years of experience working with transformer-based models and NLP tasks, with a focus on text generation, summarization, question answering, classification, and similar tasks. Expertise in transformer models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), RoBERTa, and similar models. Familiarity with model architectures, attention mechanisms, and self-attention layers that enable LLMs to generate human-like text. Experience in fine-tuning pre-trained models on domain-specific datasets for tasks such as text generation, summarization, question answering, classification, and translation. Familiarity with concepts like attention mechanisms, context windows, tokenization, and embedding layers. Awareness of biases, hallucinations, and knowledge cutoffs that can affect LLM performance and output quality. Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs. Experience in instruction-based prompting Use of zero-shot, few-shot, and many-shot learning techniques for maximizing model performance without retraining. Experience in iterating on prompts to refine outputs, test model performance, and ensure consistent results. Crafting prompt templates for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs. Expertise in chain-of-thought (CoT) prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns. Proficiency in Python and experience with NLP libraries (e.g , Hugging Face, SpaCy, NLTK). Experience with transformer-based models (e.g , GPT, BERT, T5) for text generation tasks. Experience in training, fine-tuning, and deploying machine learning models in an NLP context. Understanding of model evaluation metrics (e.g , BLEU, ROUGE) Qualification : BE/B.Tech or Equivalent degree in Computer Science or related field. Excellent communication skills in English, both verbal and written.

Responsibilities Generative AI Development: Lead the design and development of cutting-edge generative AI systems using Azure cloud services and OpenAI technologies. Focus on building efficient, scalable architectures that incorporate the latest techniques, such as transformer-based architectures and foundation models like GPT-4 and beyond. Multimodal AI Integration: Work with multimodal AI systems that can process and generate across various data types (text, images, audio, and video), leveraging large-scale embeddings and cross-modal understanding. Apply advances in diffusion models, synthetic data generation, and foundation models. Foundation Model Fine-Tuning: Apply extensive experience in fine-tuning large language models (LLMs) and foundation models, particularly those used in embeddings and vector space models. Adapt these models for specific applications, improving their efficiency and accuracy in understanding complex data structures. Azure and OpenAI Integration: Manage and optimize the deployment of generative AI projects on Azure, ensuring robust integration with OpenAI's APIs and tools. Leverage cloud-native architectures to build AI solutions that scale. Advanced Python and AI Framework Expertise: Master Python programming within the context of modern AI and machine learning libraries (e.g., PyTorch, TensorFlow, Hugging Face). Focus on developing and manipulating embeddings and vector space models using the latest advancements in machine learning and AI tools. Embeddings and Vector Space Mastery: Demonstrate deep knowledge of embedding techniques and vector space models, especially in the context of generative AI. Implement state-of-the-art strategies for creating, optimizing, and utilizing embeddings for various data types (e.g., large-scale text, images, audio). AI-Driven Personalization & Search: Lead initiatives that utilize embeddings and generative AI to develop personalized solutions, advanced search algorithms, and recommendations, leveraging AI for better data understanding and user interaction. Responsible AI & Ethics: Uphold and advocate for the responsible use of AI technologies, including ethical AI development, data privacy, bias mitigation, and alignment with compliance standards such as HIPAA, GDPR, and other relevant regulations. Model Optimization & Monitoring: Continuously optimize generative AI models, focusing on vector space efficiency, inference speed, and memory management in large-scale AI applications. Apply the latest techniques in model distillation, pruning, and efficient finetuning. Thought Leadership & Mentorship: Stay at the forefront of generative AI developments, including advancements in embeddings, LLMs, and foundation models. Mentor team members and drive knowledge sharing across interdisciplinary teams. Risk Management & Governance: Evaluate risks in generative AI initiatives, with a particular focus on embeddings and vector space models. Implement strategies to mitigate risks associated with AI model usage and performance, including ethical considerations in AI applications. Competencies Advanced degree in Computer Science, AI, Machine Learning, or a related field. Extensive experience in developing generative AI systems, with particular expertise in embeddings, vector space models, and multimodal AI. Proficiency in Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face). Demonstrated experience working with Azure and OpenAI platforms, including fine-tuning and deploying large models. Strong analytical, leadership, and communication skills with a track record of successful collaboration across interdisciplinary teams. Commitment to responsible AI practices, with knowledge of AI ethics and compliance standards (HIPAA, GDPR, etc.).

We are seeking an experienced Gen AI and LLM Developer to design, develop, and deploy generative AI models and large language models (LLMs) for advanced natural language processing (NLP) tasks. The ideal candidate will have 6-9 years of experience with LLM architectures (e.g., GPT, BERT), deep learning frameworks (PyTorch, TensorFlow), and cloud platforms (AWS, GCP, Azure). Responsibilities include training and fine-tuning models, optimizing for performance, collaborating with cross-functional teams, and staying updated on the latest AI trends. Strong technical skills in Python, NLP techniques, model optimization, and distributed training are essential.Experience designing scalable system with Generative AI" and "Exposure to full stack development on .net or Java based platform

Qualification Design, develop, and deploy machine learning and deep learning modls. Collaborate with data scientists, product managers, and engineers to identify opportunities for AI integration. Build scalable and efficient data pipelines to train and serve AI models. Optimize models for performance, accuracy, and resource efficiency. Monitor and maintain production AI systems, ensuring reliability and performance. Research and prototype new AI techniques and stay current with the latest advancements in AI and ML. Document models, processes, and methodologies clearly and concisely.

Qualification Design, develop, and deploy machine learning and deep learning modls. Collaborate with data scientists, product managers, and engineers to identify opportunities for AI integration. Build scalable and efficient data pipelines to train and serve AI models. Optimize models for performance, accuracy, and resource efficiency. Monitor and maintain production AI systems, ensuring reliability and performance. Research and prototype new AI techniques and stay current with the latest advancements in AI and ML. Document models, processes, and methodologies clearly and concisely.

We are a pioneering company dedicated to Revolutionizing autism with AI-driven solutions. Role: Develop AI models using Python and TensorFlow Implement machine learning algorithms Collaborate with data scientists to enhance AI systems Required Candidate profile Optimize AI solutions for performance Integrate AI with business applications Stay updated on AI advancements.

Mandatory (PhD Candidates Preferrable) Job Title: Data Scientist (Deep Learning & PhD experience Required) Experience: 2 Years to 8 Years MUST HAVE SKILLS REQUIRED: (MUST) Basics: Mathematics, Statistics, Physics, Bayesian Models (STRONG) Programming & DH : Python, Data Structures, Algorithms (STRONG) Data Handling : Python, EDA, Statistics, Probability Distributions (STRONG) Learning & Deep Learning : TensorFlow, PyTorch, GANs, Transformers (STRONG) Cloud & GPU: GCP, GPU-based machines, Model Optimization (STRONG) Model Lifecycle : Custom Loss Functions, Evaluation Metrics, Scalability Overview: NPCI Data Analytics team is seeking a talented and motivated Data Scientist to join our team. As a Data Scientist, you will play a crucial role in modelling complex problems, uncovering insights, and identifying new opportunities through the use of statistical, algorithmic, mining, and visualization techniques. You will be focused on utilizing NPCI product data and analytics to solve business problems by applying machine learning and deep learning techniques, conducting statistical analysis, and building high-quality predictive systems that are integrated with our services. Key Responsibilities: Develop and implement deep learning models for various applications. Work with large datasets and perform exploratory data analysis. Develop custom loss functions and evaluation metrics. Collaborate with cross-functional teams to ensure successful deployment of models. Optimize models for performance, scalability, and efficiency. Conduct research and stay up to date with the latest developments in the field of deep learning. Participate in code reviews and provide feedback to team members. Qualifications: Bachelor's or Master's degree in Computer Science, Mathematics, or related field with 3+ years of experience. Strong proficiency in Python programming language. Good understanding of data structures and algorithms. Strong understanding of statistics and probability distributions Hands-on Experience with GANs and Transformers Experience with both Tensorflow and Pytorch frameworks. Familiarity with Google Cloud Platform (GCP) and working on GPU based machines. Strong problem-solving skills. Excellent written and verbal communication skills. Ability to work independently as well as in a team environment. Strong Communication skills and ability to simplify complex information and results into simple concepts and actionable insights for a variety of audiences.

Designing and implementing groundbreaking GPU computers that run demanding deep learning, high-performance computing, and computationally intensive workloads Optimise Computer Vision Algorithms, Hardware Accelerators for performance & quality KPIs Required Candidate profile Optimize algorithms for optimal performance on the GPU tensor cores Strong Data structures and Algorithms know-how Hand-on expertise with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP)