47 Gans Jobs - Page 2

As a Senior Machine Learning Engineer, your primary role will involve designing, developing, and deploying advanced models for end-to-end content generation. This includes AI-driven image/video generation, lip syncing, and multimodal AI systems. Your focus will be on leveraging cutting-edge deep generative modeling techniques to produce highly realistic and controllable AI-generated content. You will be responsible for researching and developing state-of-the-art generative models, such as Diffusion Models, 3D VAEs, and GANs, to power AI-driven media synthesis. Additionally, you will work on building and optimizing AI pipelines for high-fidelity image/video generation and lip syncing using diffusion and autoencoder models. Your expertise will also be utilized in developing advanced lip-syncing and multimodal generation models that integrate speech, video, and facial animation for hyper-realistic AI-driven content. In addition to model development, you will implement and optimize models for real-time content generation and interactive AI applications. Collaboration with software engineers to efficiently deploy models on cloud-based architectures (AWS, GCP, or Azure) will be a key aspect of your role. Staying updated on the latest trends in deep generative models, diffusion models, and transformer-based vision systems will be essential to enhance the quality of AI-generated content. Your responsibilities will include designing and conducting experiments to evaluate model performance, improve fidelity, realism, and computational efficiency. Participation in code reviews, enhancing model efficiency, and documenting research findings for team knowledge-sharing will also be part of your duties. To qualify for this role, you should hold a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field, along with at least 3 years of experience working with deep generative models. Proficiency in Python and deep learning frameworks like PyTorch is required. Expertise in multi-modal AI, text-to-image, image-to-video generation, and audio to lip sync is essential. A strong understanding of machine learning principles, statistical methods, and problem-solving abilities are also necessary. Additionally, experience with transformers, vision-language models, cloud-based AI pipelines, and model compression techniques is advantageous. Contributions to open-source projects or published research in AI-generated content, speech synthesis, or video synthesis will be beneficial. This position offers a dynamic opportunity to work on cutting-edge AI technologies and collaborate with a team of experts in the field. If you are passionate about pushing the boundaries of AI-generated content and staying at the forefront of AI advancements, this role is ideal for you.,

AryaXAI stands at the forefront of AI innovation, revolutionizing AI for mission-critical, highly regulated industries by building explainable, safe, and aligned systems that scale responsibly. The mission of AryaXAI is to create AI tools that empower researchers, engineers, and organizations, including banks, financial institutions, and large enterprises, to unlock AI's full potential while maintaining transparency, safety, and regulatory compliance. The team at AryaXAI thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. Each team member contributes hands-on in a flat organizational structure that values curiosity, initiative, and exceptional performance, ensuring that the work not only advances technology but also meets the rigorous demands of regulated sectors. As a Senior Data Scientist at AryaXAI, you will be uniquely positioned to tackle large-scale, enterprise-level challenges in regulated environments. You will lead complex AI implementations that prioritize explainability, risk management, and compliance, directly impacting mission-critical use cases in the financial services industry and beyond. Your expertise will be crucial in deploying sophisticated models that address the nuances and stringent requirements of regulated sectors. Responsibilities include: - Model Evaluation & Customization - Architectural Assessment - Enterprise Integration - Advanced AI Techniques - Specialization & Innovation - Collaboration & Quality Assurance - Documentation & Compliance Qualifications: - Educational & Professional Background - Regulated Industry Experience (FS, Banking or Insurance is preferred) - Technical Expertise - Diverse Data Handling - Deployment Proficiency - Publications & Contributions A solid academic background in machine learning, deep learning, or reinforcement learning, ideally complemented by experience in regulated industries such as financial services or enterprise sectors, is required. The ideal candidate will have a proven track record (2+ years) of hands-on experience in data science within highly regulated environments, with a deep understanding of the unique challenges and compliance requirements in these settings. Demonstrated proficiency with deep learning frameworks (TensorFlow, PyTorch, etc.) and experience in implementing advanced techniques (Transformer models, GANs, etc.) is essential. Experience working with varied data types, including textual, tabular, categorical, and image data, and the ability to develop models that handle complex, enterprise-level datasets is also required. Expertise in deploying AI solutions in both cloud and on-premise environments, ensuring robust, scalable, and secure integrations with enterprise systems, is a key qualification. Peer-reviewed publications or significant contributions to open-source tools in AI are highly regarded.,

You are looking for a visionary Data Science Manager with expertise in Generative AI and Retrieval-Augmented Generation (RAG) to lead AI initiatives from both technical and business perspectives. In this role, you will lead a team of data scientists and ML engineers, design Generative AI models, develop statistical models, and integrate knowledge retrieval systems to enhance performance. Your responsibilities will include mentoring the team, designing scalable AI/ML solutions, implementing Generative AI models, and developing statistical models for forecasting and segmentation. You will also be responsible for integrating databases and retrieval systems, ensuring operational excellence in MLOps, and collaborating with various teams to identify high-impact use cases for GenAI. To qualify for this role, you should have a Masters in Computer Science or related fields, 10+ years of data science experience with 2+ years in GenAI initiatives, proficiency in Python and key libraries, and a strong foundation in statistical analysis and predictive modeling. Experience in cloud platforms, vector databases, and MLOps is essential, along with a background in sectors like legal tech, fintech, retail, or health tech. If you have a proven track record in building and deploying LLMs, RAG systems, and search solutions, along with a knack for influencing product roadmaps and executive strategy, this role is perfect for you. Your ability to translate complex AI concepts into actionable strategies and present findings to non-technical audiences will be crucial in driving AI/ML adoption and contributing to the company's innovation roadmap.,

Role & responsibilities Design and implement AI agent workflows. Develop end-to-end intelligent pipelines and multi-agent systems (e.g., LangGraph/LangChain workflows) that coordinate multiple LLM-powered agents to solve complex tasks. Create graph-based or state-machine architectures for AI agents, chaining prompts and tools as needed. Build and fine-tune generative models. Develop, train, and fine-tune advanced generative models (transformers, diffusion models, VAEs, GANs, etc.) on domain-specific data. Deploy and optimize foundation models (such as GPT, LLaMA, Mistral) in production, adapting them to our use cases through prompt engineering and supervised fine-tuning. Develop data pipelines. Build robust data collection, preprocessing, and synthetic data generation pipelines to feed training and inference workflows. Implement data cleansing, annotation, and augmentation processes to ensure high-quality inputs for model training and evaluation. Implement LLM-based agents and automation. Integrate generative AI agents (e.g., chatbots, AI copilots, content generators) into business processes to automate data processing and decision-making tasks. Use Retrieval-Augmented Generation (RAG) pipelines and external knowledge sources to enhance agent capabilities. Leverage multimodal inputs when applicable. Optimize performance and safety. Continuously evaluate and improve model/system performance. Use GenAI-specific benchmarks and metrics (e.g., BLEU, ROUGE, TruthfulQA) to assess results, and iterate to optimize accuracy, latency, and resource efficiency. Implement safeguards and monitoring to mitigate issues like bias, hallucination, or inappropriate outputs. Collaborate and document. Work closely with product managers, engineers, and other stakeholders to gather requirements and integrate AI solutions into production systems. Document data workflows, model architectures, and experimentation results. Maintain code and tooling (prompt libraries, model registries) to ensure reproducibility and knowledge sharing. Education: Bachelors or Master’s degree in Computer Science, Data Science, Artificial Intelligence. Programming proficiency: Expert-level skills in Python experience in machine learning and deep learning frameworks (PyTorch, TensorFlow). Generative model expertise: Demonstrated ability to build, fine-tune, and deploy large-scale generative models Familiarity with transformer architectures and generative techniques (LLMs, diffusion models, GANs) Experience working with model repositories and fine-tuning frameworks (Hugging Face, etc.). LLM and agent frameworks: Strong understanding of LLM-based systems and agent-oriented AI patterns. Experience with frameworks like LangGraph/LangChain or similar multi-agent platforms. AI integration and MLOps: Experience integrating AI components with existing systems via APIs and services. . Proficiency in retrieval-augmented generation (RAG) setups, vector databases,Familiarity with machine learning deployment and MLOps tools (Docker, Kubernetes, MLflow, KServe, etc.) Familiarity with GenAI tools: Hands-on experience with state-of-the-art GenAI models and APIs (OpenAI GPT, Anthropic, Claude, etc.) and with popular libraries (Hugging Face Transformers, LangChain, etc.). Awareness of the current GenAI tooling ecosystem and best practices.

Stay updated with cutting-edge research by analyzing and experimenting with the latest Computer Vision and AI papers. Identify AI-driven solutions to real-world problems using insights from multiple sources. Design, train, and optimize GAN-based models for image enhancement and transformation. Train, fine-tune, and deploy deep learning models for tasks like image processing, segmentation, and classification. Collaborate with data engineering teams to build scalable AI pipelines for continuous model improvement. Develop AI tools to help internal and external teams leverage innovations for customer success. Ensure models are scalable, fast, and reliable by working closely with infrastructure engineering teams. Guide and mentor junior AI engineers and researchers to enhance team productivity. Hands-on experience with GANs for image synthesis and enhancement. Strong proficiency in TensorFlow or PyTorch for deep learning model development. Experience with image processing libraries like OpenCV and PIL. Skilled in Python, C++, or C# for model programming and debugging. Practical experience deploying AI models on cloud platforms such as AWS, GCP, or Azure. Ability to analyze research papers and apply them to real-world AI applications. Familiarity with Agile methodologies and tools like Git, Jira, and Confluence. Strong communication and collaboration skills to work across teams.

Fine-tune diffusion models to enhance image quality, resolution, and style adaptability. Optimize prompt engineering techniques to refine and control image generation outputs. Develop post-processing and ranking algorithms to improve the accuracy and aesthetics of generated images. Integrate diffusion models with other AI techniques, including GANs, transformers, and hypernetworks, to expand generative capabilities. Deploy AI models into production environments, ensuring scalability and efficiency. Utilize containerization tools (Docker, Kubernetes, etc.) to streamline AI model deployment. Collaborate with AI researchers and product teams to bring AI-powered image generation solutions into real-world applications. BTech/MTech/MS in Computer Science, AI, or a related field. 2+ years of experience working with Generative AI and Computer Vision. Strong expertise in diffusion models, generative AI, and image processing. Hands-on experience with model fine-tuning and optimization. Understanding of ControlNets, hypernetworks, and AI-based prompt engineering. Proven track record in AI model deployment and scalable pipelines. Experience working with production-grade AI workflows and cloud-based solutions. Strong debugging skills Ability to troubleshoot model failures and optimize performance. Team player mindset Comfortable working with cross-functional teams. Excellent communication & presentation skills Ability to explain research concepts clearly. Comfortable working in a high-performance, in-office environment. Takes ownership from Day 1, proactively solving challenges.

As a research scientist at AryaXAI, you will be part of a team that is revolutionizing the field of AI with a focus on building explainable, safe, and aligned systems at scale. Our mission is to empower researchers, engineers, and organizations to leverage AI effectively while prioritizing transparency and safety. Our team is driven by a shared passion for innovation, collaboration, and excellence. In our flat organizational structure, everyone contributes actively to our mission, valuing curiosity, initiative, and outstanding performance. In this role, you will tackle advanced challenges in ML explainability, safety, and alignment. You will have the flexibility to specialize in specific areas within ML/DL and address various problem types related to these challenges. Additionally, you will be involved in creating new techniques around ML Observability & Alignment, collaborating with MLEs and SDEs to implement features, ensuring their quality and stability, and managing technical and product documentation. To excel in this position, you should have a solid academic foundation in machine learning and deep learning concepts. Hands-on experience with deep learning frameworks like Tensorflow and PyTorch is essential. You should enjoy working on diverse DL problems that involve different types of training datasets such as textual, tabular, categorical, and images. Proficiency in deploying code in cloud/on-premise environments, strong fundamentals in MLOps, and prior experience with ML explainability methods like LRP, SHAPE, LIME, IG, CEM are required. Ideally, you should have at least 2 years of hands-on experience in Deep Learning or Machine Learning, along with practical experience in implementing techniques such as Transformer models, GANs, and other deep learning paradigms. Join us at AryaXAI where startup culture meets research innovation, driving speed and reliability in AI technologies.,

You are an experienced professional with over 13 years of experience in engaging with clients and translating their business needs into technical solutions. You have a proven track record of working with cloud services on platforms like AWS, Azure, or GCP. Your expertise lies in utilizing AWS data services such as Redshift, Glue, Athena, and SageMaker. Additionally, you have a strong background in generative AI frameworks like GANs and VAEs and possess advanced skills in Python, including libraries like Pandas, NumPy, Scikit-learn, and TensorFlow. Your role involves designing and implementing advanced AI solutions, focusing on areas like NLP and innovative ML algorithms. You are proficient in developing and deploying NLP models and have experience in enhancing machine learning algorithms. Your knowledge extends to MLOps principles, best practices, and the development and maintenance of CI/CD pipelines. Your problem-solving skills enable you to analyze complex data sets and derive actionable insights. Moreover, your excellent communication skills allow you to effectively convey technical concepts to non-technical stakeholders. In this role, you will be responsible for understanding clients" business use cases and technical requirements, translating them into technical designs that elegantly meet their needs. You will be instrumental in mapping decisions with requirements, identifying optimal solutions, and setting guidelines for NFR considerations during project implementation. Your tasks will include writing and reviewing design documents, reviewing architecture and design aspects, and ensuring adherence to best practices. To excel in this position, you should hold a bachelor's or master's degree in computer science, Information Technology, or a related field. Additionally, relevant certifications in AI, cloud technologies, or related areas would be advantageous. Your ability to innovate, design, and implement cutting-edge solutions will be crucial in this role, as well as your skill in technology integration and problem resolution through systematic analysis. Conducting POCs to validate suggested designs and technologies will also be part of your responsibilities.,

Key Skills: Data Science, Machine Learning, NLP, Generative AI, LLM, Transformer Networks, GANs, VAEs, Prompt Engineering, Model Development, MarkLogic (preferred), Cloud-native models, Model Deployment, Data Pipelines Roles & Responsibilities: Apply deep learning and generative modeling techniques to develop LLM solutions in AI, especially in the field of Natural Language Processing (NLP). Work with various LLM technologies and Transformer Encoder Networks to enhance NLP capabilities. Design and implement state-of-the-art generative models for tasks such as text generation, text completion, language translation, and document summarization. Utilize expertise in machine learning, focusing on generative models like GANs, VAEs, and transformer-based architectures. Develop and optimize model development, model serving, and training/re-training techniques in data-sparse environments. Apply prompt engineering techniques for developing instruction-based LLMs. Collaborate with SAs and cross-functional teams to identify business requirements and deliver solutions that meet customer needs. Stay updated with the latest advancements in generative AI and LLM technologies. Evaluate and preprocess large-scale datasets, ensuring data quality and integrity, and develop data pipelines for model training and evaluation. Articulate model behavior analysis and hallucination effects to business stakeholders. Develop guardrails for LLMs, leveraging both open-source and cloud-native models. Collaborate with software engineers to deploy and optimize generative models in production environments. Mentor junior data scientists and contribute to the growth of the data science team. Experience Requirement: 4-10 years of experience working in Data Science, Machine Learning, and especially NLP technologies. Experience with LLM technologies and solid understanding of Transformer Encoder Networks. Proven experience with generative models, including GANs, VAEs, and transformer-based architectures. Familiarity with model development, training/re-training techniques, and deployment in data-sparse environments. Strong understanding of prompt engineering techniques for LLMs. Experience with data preprocessing and building data pipelines for generative models. Ability to explain model behaviors, hallucination effects, and behavioral analysis techniques to stakeholders. Experience with developing guardrails for LLMs using open-source or cloud-native models. Exposure to MarkLogic is a plus. Education: Any Graduation.

Develop and deploy advanced machine learning models to address business problems across multiple domains. Analyze large datasets, perform exploratory data analysis (EDA), and derive actionable insights. Build, test, and refine predictive models and algorithms using a variety of ML techniques (e.g., supervised and unsupervised learning, deep learning,computer vision). AI & Gen AI Expertise: Apply cutting-edge generative AI models (e.g., GPT, VAEs, GANs) to create innovative solutions and products. Work with AI agents to automate and optimize workflows across different areas of the business. Explore and implement novel AI methodologies, including reinforcement learning and transfer learning. Cloud Infrastructure: Design and implement scalable data pipelines and ML models in cloud environments such as AWS or Azure. Collaborate with the cloud infrastructure team to ensure smooth deployment and operation of models in production. Leverage cloud-native AI and ML tools (e.g., AWS SageMaker, Azure ML) to accelerate model development and deployment. Collaboration & Leadership: Work closely with cross-functional teams, including software engineers, data engineers, and product managers, to integrate AI solutions into products. Stay Up-to-date with Trends: Continuously research and evaluate the latest AI advancements, trends in machine learning, and emerging technologies in the AI space. Contribute to internal knowledge-sharing, promoting the latest findings, tools, and techniques to improve team capabilities. Key Requirements: Education: Bachelors in Computer Science, Data Science, Engineering, Mathematics, or a related field. Experience: 5+ years of relevant hands-on experience as a Data Scientist or Machine Learning Engineer. Proven track record of developing and deploying machine learning models in a production environment. Experience with generative AI models (GPT, GANs, VAEs) and AI agents is a must. Strong knowledge of cloud platforms (AWS, Azure) and their AI/ML services (SageMaker, Azure ML, etc.). Solid understanding of the latest trends in AI, including large language models, reinforcement learning, computer vision & deep learning. Technical Skills: Proficiency in Python, R, or other relevant programming languages. Strong knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras. Experience with SQL and cloud-native data processing tools (e.g., AWS Redshift, Azure Synapse, Spark). Familiarity with DevOps practices and CI/CD pipelines for ML model deployment. Soft Skills: Strong communication skills with the ability to translate complex technical concepts into business-friendly language. Problem-solving mindset, with the ability to approach challenges creatively and collaborate with diverse teams. Leadership potential or experience mentoring junior team members. Preferred Qualifications: Certification or training in AWS (e.g., AWS Certified Machine Learning), Azure, or other cloud services. Experience working with containerization technologies like Docker and Kubernetes for model deployment. Exposure to the latest trends in AI ethics, explainability, and fairness.

Oracle Database Performance Specialist - ( AI/ML Engineer) About Oracle - Customer Success Services Oracle is the only provider of fully integrated technology solutions that span both infrastructure and applications. Today's businesses, however, need more than just the best technology solutions. They need a strategic partner to support sustained business growth. Oracle Customer Success Services (CSS) was created to help ensure customer's ongoing success with our technology. CSS is completely integrated with Oracle's product development teams to help maximize the value of customer's cloud investment. Job Responsibilities: As CSS AI Engineer, Work directly with customers and have a solid understanding of the application development and support processes. you will work closely with cross-functional teams to design, build, and deploy AI solutions in a scalable and efficient manner. You will be responsible for leveraging Docker and Kubernetes for containerization and orchestration, implementing MLOps practices and utilizing your skills in python and machine learning to build and enhance our AI models. Experience in Generative AI is highly desired as we continue to push the boundaries of AI applications . Design and develop AI/ML models and systems from prototyping to production, while ensuring scalability and efficiency. Utilize Docker and Kubernetes for containerization, deployment and orchestration of AI models and services in production environments. Implement and manage end-to-end MLOps pipelines to automate model training, testing, deployment and monitoring. Build robust, reusable, and scalable Python code for AI model development and deployment. Work on the integration and deployment of generative AI models and systems for innovative applications. Troubleshoot, optimize and maintain production-level AI models and services. Stay up-to-date with the latest trends and advancements in AI, machine learning and MLOps technologies. Mandatory Skills: Bachelor's or Master's degree in Computer Science, Engineering or related field. Proven experience (3+ years) in AI/ML engineering with a strong focus on Docker, Kubernetes, and MLOps. Expertise in Python and frameworks/libraries such as TensorFlow, PyTorch, Scikit-learn or similar. Solid understanding of machine learning algorithms and techniques, particularly in deep learning and natural language processing (NLP). Hands-on experience deploying and managing AI models in production using Docker and Kubernetes. Knowledge of cloud platforms (OCI, AWS, Azure, GCP) and how they relate to containerized AI deployments. Experience with CI/CD pipelines and version control (Git). Strong understanding of generative AI models such as GANs, VAEs or transformers. Familiarity with monitoring, logging, and debugging AI/ML systems in production environments. Excellent communication and teamwork skills, with the ability to collaborate with diverse teams. If you have the above skills, take up the below list of self-test questions to know if you qualify to apply. Self-Test Questions: Is supervised learning used when the data is labelled Answer : Yes, Supervised learning requires a labelled dataset, where each training example has a corresponding output label. Can overfitting occur when a model is too complex Answer : Yes, Overfitting happens when a model is excessively complex, capturing noise in the training data rather than generalizing to new data. Is decision tree a type of supervised learning algorithm Answer : Yes, A decision tree is a supervised learning algorithm used for both classification and regression tasks. Can gradient descent be used for optimization in neural networks Answer : Yes, Gradient descent is commonly used to optimize the weights in neural networks by minimizing the loss function. Is normalization used to scale features between 0 and 1 Answer: Yes, Normalization scales the data to a specific range, often between 0 and 1, to improve the performance of machine learning algorithms. Oracle Database Performance Specialist - ( AI/ML Engineer) About Oracle - Customer Success Services Oracle is the only provider of fully integrated technology solutions that span both infrastructure and applications. Today's businesses, however, need more than just the best technology solutions. They need a strategic partner to support sustained business growth. Oracle Customer Success Services (CSS) was created to help ensure customer's ongoing success with our technology. CSS is completely integrated with Oracle's product development teams to help maximize the value of customer's cloud investment. Job Responsibilities: As CSS AI Engineer, Work directly with customers and have a solid understanding of the application development and support processes. you will work closely with cross-functional teams to design, build, and deploy AI solutions in a scalable and efficient manner. You will be responsible for leveraging Docker and Kubernetes for containerization and orchestration, implementing MLOps practices and utilizing your skills in python and machine learning to build and enhance our AI models. Experience in Generative AI is highly desired as we continue to push the boundaries of AI applications . Design and develop AI/ML models and systems from prototyping to production, while ensuring scalability and efficiency. Utilize Docker and Kubernetes for containerization, deployment and orchestration of AI models and services in production environments. Implement and manage end-to-end MLOps pipelines to automate model training, testing, deployment and monitoring. Build robust, reusable, and scalable Python code for AI model development and deployment. Work on the integration and deployment of generative AI models and systems for innovative applications. Troubleshoot, optimize and maintain production-level AI models and services. Stay up-to-date with the latest trends and advancements in AI, machine learning and MLOps technologies. Mandatory Skills: Bachelor's or Master's degree in Computer Science, Engineering or related field. Proven experience (3+ years) in AI/ML engineering with a strong focus on Docker, Kubernetes, and MLOps. Expertise in Python and frameworks/libraries such as TensorFlow, PyTorch, Scikit-learn or similar. Solid understanding of machine learning algorithms and techniques, particularly in deep learning and natural language processing (NLP). Hands-on experience deploying and managing AI models in production using Docker and Kubernetes. Knowledge of cloud platforms (OCI, AWS, Azure, GCP) and how they relate to containerized AI deployments. Experience with CI/CD pipelines and version control (Git). Strong understanding of generative AI models such as GANs, VAEs or transformers. Familiarity with monitoring, logging, and debugging AI/ML systems in production environments. Excellent communication and teamwork skills, with the ability to collaborate with diverse teams. If you have the above skills, take up the below list of self-test questions to know if you qualify to apply. Self-Test Questions: Is supervised learning used when the data is labelled Answer : Yes, Supervised learning requires a labelled dataset, where each training example has a corresponding output label. Can overfitting occur when a model is too complex Answer : Yes, Overfitting happens when a model is excessively complex, capturing noise in the training data rather than generalizing to new data. Is decision tree a type of supervised learning algorithm Answer : Yes, A decision tree is a supervised learning algorithm used for both classification and regression tasks. Can gradient descent be used for optimization in neural networks Answer : Yes, Gradient descent is commonly used to optimize the weights in neural networks by minimizing the loss function. Is normalization used to scale features between 0 and 1 Answer: Yes, Normalization scales the data to a specific range, often between 0 and 1, to improve the performance of machine learning algorithms. Career Level - IC3

Oracle Database Performance Specialist - ( AI/ML Engineer) About Oracle -Customer Success Services Oracle is the only provider of fully integrated technology solutions that span both infrastructure and applications. Today's businesses, however, need more than just the best technology solutions. They need a strategic partner to support sustained business growth. Oracle Customer Success Services (CSS) was created to help ensure customer's ongoing success with our technology. CSS is completely integrated with Oracle's product development teams to help maximize the value of customer's cloud investment. Job Responsibilities: As CSS AI Engineer, Work directly with customers and have a solid understanding of the application development and support processes. you will work closely with cross-functional teams to design, build, and deploy AI solutions in a scalable and efficient manner. You will be responsible for leveraging Docker and Kubernetes for containerization and orchestration, implementing MLOps practices and utilizing your skills in python and machine learning to build and enhance our AI models. Experience in Generative AI is highly desired as we continue to push the boundaries of AI applications. Design and develop AI/ML models and systems from prototyping to production, while ensuring scalability and efficiency. Utilize Docker and Kubernetes for containerization, deployment and orchestration of AI models and services in production environments. Implement and manage end-to-end MLOps pipelines to automate model training, testing, deployment and monitoring. Build robust, reusable, and scalable Python code for AI model development and deployment. Work on the integration and deployment of generative AI models and systems for innovative applications. Troubleshoot, optimize and maintain production-level AI models and services. Stay up-to-date with the latest trends and advancements in AI, machine learning and MLOps technologies. Mandatory Skills: Bachelor's or Master's degree in Computer Science, Engineering or related field. Proven experience (3+ years) in AI/ML engineering with a strong focus on Docker, Kubernetes, and MLOps. Expertise in Python and frameworks/libraries such as TensorFlow, PyTorch, Scikit-learn or similar. Solid understanding of machine learning algorithms and techniques, particularly in deep learning and natural language processing (NLP). Hands-on experience deploying and managing AI models in production using Docker and Kubernetes. Knowledge of cloud platforms (OCI, AWS, Azure, GCP) and how they relate to containerized AI deployments. Experience with CI/CD pipelines and version control (Git). Strong understanding of generative AI models such as GANs, VAEs or transformers. Familiarity with monitoring, logging, and debugging AI/ML systems in production environments. Excellent communication and teamwork skills, with the ability to collaborate with diverse teams. If you have the above skills, take up the below list of self-test questions to know if you qualify to apply. Self-Test Questions: Is supervised learning used when the data is labelled Answer : Yes, Supervised learning requires a labelled dataset, where each training example has a corresponding output label. Can overfitting occur when a model is too complex Answer : Yes, Overfitting happens when a model is excessively complex, capturing noise in the training data rather than generalizing to new data. Is decision tree a type of supervised learning algorithm Answer : Yes, A decision tree is a supervised learning algorithm used for both classification and regression tasks. Can gradient descent be used for optimization in neural networks Answer : Yes, Gradient descent is commonly used to optimize the weights in neural networks by minimizing the loss function. Is normalization used to scale features between 0 and 1 Answer: Yes, Normalization scales the data to a specific range, often between 0 and 1, to improve the performance of machine learning algorithms. Oracle Database Performance Specialist - ( AI/ML Engineer) About Oracle -Customer Success Services Oracle is the only provider of fully integrated technology solutions that span both infrastructure and applications. Today's businesses, however, need more than just the best technology solutions. They need a strategic partner to support sustained business growth. Oracle Customer Success Services (CSS) was created to help ensure customer's ongoing success with our technology. CSS is completely integrated with Oracle's product development teams to help maximize the value of customer's cloud investment. Job Responsibilities: As CSS AI Engineer, Work directly with customers and have a solid understanding of the application development and support processes. you will work closely with cross-functional teams to design, build, and deploy AI solutions in a scalable and efficient manner. You will be responsible for leveraging Docker and Kubernetes for containerization and orchestration, implementing MLOps practices and utilizing your skills in python and machine learning to build and enhance our AI models. Experience in Generative AI is highly desired as we continue to push the boundaries of AI applications. Design and develop AI/ML models and systems from prototyping to production, while ensuring scalability and efficiency. Utilize Docker and Kubernetes for containerization, deployment and orchestration of AI models and services in production environments. Implement and manage end-to-end MLOps pipelines to automate model training, testing, deployment and monitoring. Build robust, reusable, and scalable Python code for AI model development and deployment. Work on the integration and deployment of generative AI models and systems for innovative applications. Troubleshoot, optimize and maintain production-level AI models and services. Stay up-to-date with the latest trends and advancements in AI, machine learning and MLOps technologies. Mandatory Skills: Bachelor's or Master's degree in Computer Science, Engineering or related field. Proven experience (3+ years) in AI/ML engineering with a strong focus on Docker, Kubernetes, and MLOps. Expertise in Python and frameworks/libraries such as TensorFlow, PyTorch, Scikit-learn or similar. Solid understanding of machine learning algorithms and techniques, particularly in deep learning and natural language processing (NLP). Hands-on experience deploying and managing AI models in production using Docker and Kubernetes. Knowledge of cloud platforms (OCI, AWS, Azure, GCP) and how they relate to containerized AI deployments. Experience with CI/CD pipelines and version control (Git). Strong understanding of generative AI models such as GANs, VAEs or transformers. Familiarity with monitoring, logging, and debugging AI/ML systems in production environments. Excellent communication and teamwork skills, with the ability to collaborate with diverse teams. If you have the above skills, take up the below list of self-test questions to know if you qualify to apply. Self-Test Questions: Is supervised learning used when the data is labelled Answer : Yes, Supervised learning requires a labelled dataset, where each training example has a corresponding output label. Can overfitting occur when a model is too complex Answer : Yes, Overfitting happens when a model is excessively complex, capturing noise in the training data rather than generalizing to new data. Is decision tree a type of supervised learning algorithm Answer : Yes, A decision tree is a supervised learning algorithm used for both classification and regression tasks. Can gradient descent be used for optimization in neural networks Answer : Yes, Gradient descent is commonly used to optimize the weights in neural networks by minimizing the loss function. Is normalization used to scale features between 0 and 1 Answer: Yes, Normalization scales the data to a specific range, often between 0 and 1, to improve the performance of machine learning algorithms. Career Level - IC3

AI / Machine Learning / Data Science part time Work from Home (Any where in world) Warm Greetings from Excel Online Classes, We are a team of industry professionals running an institute that provides comprehensive online IT training, technical support, and development services. We are currently seeking AI / Machine Learning / Data Science Experts who are passionate about technology and can collaborate with us in their free time. If you're enthusiastic, committed, and ready to share your expertise, we would love to work with you! Were hiring for the following services: Online Training Online Development Online Technical Support Conducting Online Interviews Corporate Training Proof of Concept (POC) Projects Research & Development (R&D) We are looking for immediate joiners who can contribute in any of the above areas. If you're interested, please fill out the form using the link below: https://docs.google.com/forms/d/e/1FAIpQLSdvut0tujgMbBIQSc6M7qldtcjv8oL1ob5lBc2AlJNRAgD3Cw/viewform We also welcome referrals! If you know someone—friends, colleagues, or connections—who might be interested in: Teaching, developing, or providing tech support online Sharing domain knowledge (e.g., Banking, Insurance, etc.) Teaching foreign languages (e.g., Spanish, German, etc.) Learning or brushing up on technologies to clear interviews quickly Upskilling in new tools or frameworks for career growth Please feel free to forward this opportunity to them. For any queries, feel free to contact us at: excel.onlineclasses@gmail.com Thank you & Best Regards, Team Excel Online Classes excel.onlineclasses@gmail.com

Skills Required Deep Learning Expertise: Strong understanding of deep learning concepts, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), transfer learning, etc. Proficiency in Machine Learning Frameworks: Hands-on experience with popular frameworks such as TensorFlow, Keras, PyTorch, or MXNet. Mathematics for Machine Learning: Solid knowledge of mathematical foundations, including linear algebra, probability, statistics, and optimization algorithms related to deep learning. Experience in Model Building: Ability to build, train, and optimize deep learning models for different use cases like image classification, NLP, speech recognition, etc. Content Development Skills: Experience in creating comprehensive learning materials, including video lectures, assignments, quizzes, and projects for students. Programming Proficiency: Strong knowledge of Python and libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn. Cloud and Deployment: Familiarity with deploying deep learning models on cloud platforms like AWS, GCP, or Azure. Hands-on Projects: Experience in creating hands-on projects to help students apply learned concepts practically. Pedagogy & Curriculum Development: Ability to structure and organize technical content clearly and logically to suit a range of learning levels. Communication Skills: Excellent written and verbal communication skills to explain complex technical concepts simply and effectively. Collaboration Skills: Ability to work with instructors, instructional designers, and subject matter experts to create engaging and accurate content. Prior experience in content development is preferred. Responsibilities Assist the Technical Content Developer (TCD) in creating high-quality technical content across various formats such as text, video scripts, presentations, and interactive materials. Support the TCD in researching and gathering resources to ensure the accuracy and relevance of the content. Collaborate with Subject Matter Experts (SMEs) and other team members to refine and polish content for final delivery. Contribute to developing curriculum outlines, roadmaps, and learning objectives under the guidance of the TCD. Ensure content is optimized for different learning formats, adapting materials as required. Participate in regular content reviews and provide suggestions for improvements or updates based on learner feedback. Stay updated on industry trends and assist in incorporating emerging technologies and techniques into the content. Manage smaller content projects independently while maintaining alignment with the overall curriculum goals. Job Overview Location : NxtWave Office Spaces in Hyderabad Job Type : Full-Time Working days : 5

Job Description: Generative AI Expertise In depth knowledge of various Generative AI techniques including GANs Generative Adversarial Networks VAEs Variational Autoencoders and other relevant architectures Experience with both image and text generation is essential Conversant with Gen AI development tools like Prompt engineering Langchain Semantic Kernels Function calling Exposure to both API based and opens source LLMs based solution design Responsible AI Should have proficient knowledge in Responsible AI and Data Privacy principles to ensure ethical data handling transparency and accountability in all stages of AI development Must demonstrate a commitment to upholding privacy standards mitigating bias and fostering trust within data driven initiatives Machine Learning Mastery Profound understanding of machine learning principles algorithms and frameworks Able to design and implement models optimize performance and manage training pipelines effectively Technical Proficiency Proficiency in programming languages commonly used in AI development such as Python TensorFlow PyTorch or similar tools Experience with cloud platforms e g AWS Azure GCP and distributed computing is advantageous Architecture Design Ability to design end to end Generative AI architectures that encompass data preprocessing model selection training pipelines and deployment strategies Strong grasp of scalable reliable and efficient system design Key Responsibilities: A day in the life of an Infoscion As part of the Infosys consulting team your primary role would be to lead the engagement effort of providing high quality and value adding consulting solutions to customers at different stages from problem definition to diagnosis to solution design development and deployment You will review the proposals prepared by consultants provide guidance and analyze the solutions defined for the client business problems to identify any potential risks and issues You will identify change Management requirements and propose a structured approach to client for managing the change using multiple communication mechanisms You will also coach and create a vision for the team provide subject matter training for your focus areas motivate and inspire team members through effective and timely feedback and recognition for high performance You would be a key contributor in unit level and organizational initiatives with an objective of providing high quality value adding consulting solutions to customers adhering to the guidelines and processes of the organization If you think you fit right in to help our clients navigate their next in their digital transformation journey this is the place for you Technical Requirements: Primary skills Technology Artificial Intelligence Computer Vision Technology Big Data Natural language processing NLP Technology Machine Learning Python Technology Machine Learning TensorFlow Additional Responsibilities: Good knowledge on software configuration management systems Strong business acumen strategy and cross industry thought leadership Awareness of latest technologies and Industry trends Logical thinking and problem solving skills along with an ability to collaborate Two or three industry domain knowledge Understanding of the financial processes for various types of projects and the various pricing models available Client Interfacing skills Knowledge of SDLC and agile methodologies Project and Team management Preferred Skills: Technology->Artificial Intelligence->Computer Vision,Technology->Big Data->Natural language processing(NLP),Technology->Machine Learning->Python,Technology->Machine Learning->TensorFlow

Role & responsibilities • Familiarity with Mainframe and Midrange platforms is essential for integrating Gen AI solutions into legacy enterprise systems. Candidate should understand data structures, interfaces, and operational workflows on platforms like IBM Z(Mainframe) and IBM I (Midrange). Experience in modernizing and bridging AI applications with these systems is a strong advantage. masters or PhD in Computer Science, Machine Learning, or related field (or equivalent experience) 5+ years in AI/ML development, with 3+ years focused on generative AI (LLMs, GANs, VAEs, etc.) Proven track record of deploying generative AI solutions in production environments mandatory technical skills: deep expertise in generative architectures (GPT, Transformers, Diffusion Models) Experience with NLP tools (Hugging Face, spaCy) and vector databases (Pinecone, FAISS) mandatory proficiency in Python, PyTorch/TensorFlow, and cloud platforms preferred: knowledge of multimodal AI (text-to-image, video synthesis) experience with reinforcement learning (RLHF) for model alignment preferred familiarity with AI ethics frameworks (e.g., AI Fairness 360) strong analytical and problem-solving skills, excellent communication and collaboration abilities, attention to detail and ability to work independently Interested candidate can share me resume in recruiter.wtr26@walikingtree.in

Generative AI Engineer Python and FastAPI,deploying generative AI models (LLMs, GANs, VAEs) in production environmentsRAG, embeddings,RESTful APIsmanaging secure APIDocker and KubernetesSQL and NoSQL databases(AWS, Azure, GCP)

Job Summary: We are seeking a passionate and technically skilled AI / ML Engineer with 2+ years of hands-on experience in Computer Vision and Generative AI (GenAI). This role involves building and optimizing advanced visual intelligence systems leveraging PyTorch, OpenCV, transformers, and diffusion models. The ideal candidate will have experience in areas such as image segmentation, pose estimation, video inpainting, and synthetic data generation, along with exposure to LLM-prompt integration and datasets like DeepFashion2 and COCO. Key Responsibilities: Develop and deploy advanced computer vision models for tasks such as image segmentation, pose estimation, and video manipulation. Implement and experiment with Generative AI techniques, including diffusion models, GANs, and video inpainting. Leverage PyTorch, OpenCV, and modern deep learning frameworks to build scalable vision pipelines. Integrate LLM-based prompting into visual workflows and multimodal applications. Utilize datasets such as DeepFashion2, COCO, and custom synthetic datasets for model training and validation. Optimize model inference for performance, latency, and resource efficiency in production environments. Collaborate with data scientists, product engineers, and designers to deliver intelligent visual features in end-user applications. Qualifications: 2+ years of experience in machine learning and computer vision. Proficiency with PyTorch, OpenCV, and relevant deep learning libraries. Hands-on experience with image segmentation, pose estimation, or video-based vision tasks. Understanding of diffusion models, GANs, and transformer-based architectres. Knowledge of LLM prompt engineering and multimodal integration. Experience working with structured datasets like COCO and DeepFashion2. Strong debugging, analytical, and performance optimization skills. Preferred Qualifications : Experience in synthetic data generation and domain-specific data augmentation. Familiarity with model quantization, pruning, or other inference optimization techniques. Exposure to MLOps tools and cloud-based model deployment (e.g., AWS Sagemaker, GCP AI Platform). Contributions to open-source projects or published research in computer vision or generative models. Location: Pan- Bengaluru,Hyderabad,Delhi / NCR,Chennai,Pune,Kolkata,Ahmedabad,Mumbai

Gen AI + DS + ML Ops Job Title: Generative AI and Data Science Engineer with MLOps Expertise Location: Gurgaon, India Employment Type: Full-time About the Role: We are seeking a versatile and highly skilled Generative AI and Data Science Engineer with strong MLOps expertise. This role combines deep technical knowledge in data science and machine learning with a focus on designing and deploying scalable, production-level AI solutions. You will work with cross-functional teams to drive AI/ML projects from research and prototyping through to deployment and maintenance, ensuring model robustness, scalability, and efficiency. Responsibilities: Generative AI Development and Data Science: Design, develop, and fine-tune generative AI models for various applications such as natural language processing, image synthesis, and data augmentation. Perform exploratory data analysis (EDA) and statistical modeling to identify trends, patterns, and actionable insights. Collaborate with data engineering and product teams to create data pipelines for model training, testing, and deployment. Apply data science techniques to optimize model performance and address real-world business challenges. Machine Learning Operations (MLOps): Implement MLOps best practices for managing and automating the end-to-end machine learning lifecycle, including model versioning, monitoring, and retraining. Build, maintain, and optimize CI/CD pipelines for ML models to streamline development and deployment processes. Ensure scalability, robustness, and security of AI/ML systems in production environments. Develop tools and frameworks for monitoring model performance and detecting anomalies post-deployment. Research and Innovation: Stay current with advancements in generative AI, machine learning, and MLOps technologies and frameworks. Identify new methodologies, tools, and technologies that could enhance our AI and data science capabilities. Engage in R&D initiatives and collaborate with team members on innovative projects. Requirements: Educational Background: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. PhD is a plus. Technical Skills: Proficiency in Python and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn). Strong understanding of generative AI models (e.g., GANs, VAEs, transformers) and deep learning techniques. Experience with MLOps frameworks and tools such as MLflow, Kubeflow, Docker, and CI/CD platforms. Knowledge of data science techniques for EDA, feature engineering, statistical modeling, and model evaluation. Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying and scaling AI/ML models. Soft Skills: Ability to collaborate effectively across teams and communicate complex technical concepts to non-technical stakeholders. Strong problem-solving skills and the ability to innovate in a fast-paced environment. Preferred Qualifications: Prior experience in designing and deploying large-scale generative AI models. Proficiency in SQL and data visualization tools (e.g., Tableau, Power BI). Experience with model interpretability and explainability frameworks.

Job Summary: We are seeking a visionary and hands-on AI Architect with 8+ years of experience in designing and deploying AI-driven systems, including Generative AI (GenAI), LLM integration, computer vision, and agentic workflows. This role demands deep technical expertise in transformer models, PyTorch, and tools such as Stable Diffusion, ControlNet, and LangChain, as well as a strong grasp of dataset strategy, bias mitigation, and scalable AI system architecture. Experience with e-commerce or fashion datasets is a strong plus. Key Responsibilities: Architect and lead the development of AI/ML systems, with a focus on Generative AI, including diffusion models, GANs, and LLMs. Implement and optimize models such as Stable Diffusion, ControlNet, and OpenPose for real-world use cases in visual content generation. Design LLM-integrated solutions using LangChain, agentic workflows, and multimodal AI systems. Collaborate with cross-functional teams to define dataset strategy, ensuring data relevance, diversity, and quality. Integrate Hugging Face Transformers and PyTorch-based architectures into production environments. Define and implement best practices around bias & fairness, model explainability, and ethical AI design. Lead architectural decisions for scalable AI platforms, focusing on performance, reliability, and continuous learning capabilities. Leverage domain knowledge in e-commerce/fashion for visual understanding, recommendation, or personalization solutions. Qualifications: 8+ years of experience in AI/ML system architecture, including 3+ years specifically in Generative AI and LLM ecosystems. Proficiency in PyTorch, Transformers, Hugging Face, and LangChain. Deep understanding of diffusion models, GANs, ControlNet, and OpenPose. Experience in designing agentic AI workflows, real-time inference systems, and production-grade AI infrastructure. Proven track record in dataset creation/curation, bias analysis, and fairness optimization. Strong communication skills and the ability to translate complex AI concepts into business impact. Preferred Qualifications : Prior experience with e-commerce, fashion technology, or retail-based AI datasets. Contributions to open-source AI frameworks or publications in relevant research areas. Familiarity with cloud AI services (e.g., AWS Sagemaker, Azure AI, or GCP Vertex AI). Understanding of AI observability, versioning, and model governance. Location: Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad

Immediate opening for "AI Architect (Generative AI)" Location : Remote Experience :- 15 to 23 yrs Quantaleap INC. is seeking a highly experienced AI Architect with deep expertise in Generative AI to drive innovative solution design and architecture for enterprise use cases. This role requires a strategic thinker with a strong foundation in AI/ML technologies and a proven ability to lead high-impact AI initiatives across diverse domains. Key Responsibilities: Architect end-to-end AI solutions leveraging Generative AI technologies (LLMs, diffusion models, transformers, etc.). Evaluate and recommend cutting-edge tools, frameworks, and models suitable for enterprise-grade deployments. Collaborate with cross-functional teams for RFP responses, technical proposals, and client presentations. Lead the development of scalable AI solutions from POC to production across multiple industry verticals. Guide technical teams on best practices in AI model development, fine-tuning, and deployment. Must-Have Skills: 15+ years of overall experience with a strong focus on AI/ML architecture In-depth knowledge of Generative AI technologies, including LLMs (GPT, BERT), GANs, and foundation models Experience in deploying AI models in cloud-native and on-prem environments Strong understanding of MLOps, data pipelines, and model lifecycle management Proven track record in presales, RFP responses, and enterprise solutioning Excellent stakeholder management and client-facing communication skills Interested can share their updated resume to anitha.mudaliyar@quantaleap.com

Roles and Responsibilities Design, develop, and deploy advanced AI models with a focus on generative AI, including transformer architectures (e.g., GPT, BERT, T5) and other deep learning models used for text, image, or multimodal generation. Work with extensive and complex datasets, performing tasks such as cleaning, preprocessing, and transforming data to meet quality and relevance standards for generative model training. Collaborate with cross-functional teams (e.g., product, engineering, data science) to identify project objectives and create solutions using generative AI tailored to business needs. Implement, fine-tune, and scale generative AI models in production environments, ensuring robust model performance and efficient resource utilization. Develop pipelines and frameworks for efficient data ingestion, model training, evaluation, and deployment, including A/B testing and monitoring of generative models in production. Stay informed about the latest advancements in generative AI research, techniques, and tools, applying new findings to improve model performance, usability, and scalability. Documentandcommunicatetechnicalspecifications, algorithms, and project outcomes to technical and non-technical stakeholders, with an emphasis on explainability and responsible AI practices. Qualifications Required Educational Background: Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or a related field. Relevant Ph.D. or research experience in generative AI is a plus. Experience: 8-12 years of experience in machine learning, with 2+ years in designing and implementing generative AI models or working specifically with transformer-based models. Skills and Experience Required GenerativeAI: Transformer Models, GANs, VAEs, Text Generation, Image Generation Machine Learning: Algorithms, Deep Learning, Neural Networks Programming: Python, SQL; familiarity with libraries such as Hugging Face Transformers, PyTorch, Tensor Flow MLOps: Docker, Kubernetes, MLflow, Cloud Platforms (AWS, GCP, Azure) Data Engineering: Data Preprocessing, Feature Engineering, Data Cleaning Why you'll love working with us: Opportunity to work on technical challenges with global impact. Vast opportunities for self-development, including online university access and sponsored certifications. Sponsored Tech Talks &Hackathons to foster innovation and learning. Generous benefits package including health insurance, retirement benefits, flexible work hours, and more. Private and Confidential www.fissionlabs.com info@fissionlabs.com Supportive work environment with forums to explore passions beyond work. This role presents an exciting opportunity for a motivated individual to contribute to the development of cutting-edge solutions while advancing their career in a dynamic and collaborative environment.