17 Vaes Jobs

At Murf AI, we're simplifying multimedia creation by harnessing the power of artificial intelligence. Our platform empowers users to craft high-quality voiceovers effortlessly, without the need for recording equipment. Some interesting facts about Murf AI: Customers in 100+ countries 1Mn+ registered users 6X growth in revenue in the last 12 months 120+ voices in 20+ languages offered by Murf Studio Consistently rated a Leader and Momentum Leader on G2 in the text-to-speech category in 2023 and 2022 Working on bleeding-edge research problems in synthetic speech, synthetic video, and dubbing. We are seeking a highly skilled and innovative Research Scientist specializing in Generative Video AI to join our dynamic team. As a Research Scientist, you will play a pivotal role in developing cutting-edge algorithms and models for creating synthetic videos, avatars, and lip syncing applications. Your expertise in AI, computer vision, and image processing will be instrumental in advancing our technology and pushing the boundaries of generative video AI. This is an exciting opportunity to work at the forefront of AI research and contribute to groundbreaking advancements in the field. An ideal candidate will hold a Ph.D. in AI or Computer Science with a specialization in Computer Vision, Image Processing, or related fields with 3+ years of additional relevant work experience. Roles & Responsibilities: Conduct research in the field of generative video AI, focusing on synthetic videos, avatars, and lip-syncing use-cases Develop algorithms and models based on state-of-the-art computer vision and machine learning advancements, especially in the areas of generative AI (e.g. image, video, or 3D generation) Collaborate with cross-functional teams to understand requirements, identify research problems, and propose innovative solutions Collect and curate large-scale datasets for training and evaluating video generation models Develop and optimize computational pipelines for video generation, ensuring efficiency and scalability Stay up-to-date with the latest advancements in generative video AI, attending conferences, publishing research papers, and actively contributing to the scientific community Provide technical guidance and mentorship to junior researchers and engineers Collaborate with product teams to integrate research findings into practical applications and drive commercialization efforts Contribute to patent filing and intellectual property protection activities, as applicable Skills & Qualifications: Strong background in generative models, deep learning such as GANs, NeRFs, VAEs, Diffusion Models, etc. Solid understanding of image processing and computer vision techniques in general Ability to conduct original research and first principles problem-solving Ability to collaborate effectively in a team environment and lead research initiatives Proficiency in programming using Python and either TensorFlow or PyTorch Proven publications in computer vision or artificial intelligence peer-reviewed top-tier conferences (e.g. CVPR, ECCV, ICCV, NeurIPS, ICLR, or SIGGRAPH, etc.) or leading Journals (LNCS, PAMI, TOG, etc.) published by IEEE, ACM, Springer, Elsevier, etc. Experience with large-scale data collection and curation is a plus Benefits/Perks: Collaborative and stimulating work environment Opportunity to work with cutting-edge technologies and make a significant impact in the field of generative video AI Insurance/healthcare coverage as per company policy Leaves available as per company policy Why Murf.ai Murf AI is revolutionizing creative workflows for businesses through the utilization of our cutting-edge AI technology. Recognized as a consistent Leader and Momentum Leader on G2 in 2022 and 2023, we empower L&D and creative teams to evolve into dynamic creative powerhouses. With a focus on cutting-edge research and innovation, a positive work culture, and real impact in the AI-driven space, Murf.ai is one of the fastest-growing companies in the AI voice space. Join us at this pivotal moment, where your ideas matter, and your leadership can leave a lasting impact. If you're ready for a challenge and have the vision to lead from the front, Murf.ai is where your journey into the next chapter of AI innovation begins.,

We are looking for a highly experienced Voice AI /ML Engineer to take the lead in designing and deploying real-time voice intelligence systems. This position specifically involves working on ASR, TTS, speaker diarization, wake word detection, and developing production-grade modular audio processing pipelines to support next-generation contact center solutions, intelligent voice agents, and high-quality audio systems. You will be operating at the convergence of deep learning, streaming infrastructure, and speech/NLP technology, with a focus on creating scalable, low-latency systems that cater to diverse audio formats and real-world applications. Your responsibilities will include: - Building, fine-tuning, and deploying ASR models such as Whisper, wav2vec2.0, and Conformer for real-time transcription. - Developing high-quality TTS systems using VITS, Tacotron, FastSpeech for natural-sounding voice generation. - Implementing speaker diarization to segment and identify speakers in multi-party conversations using embeddings and clustering techniques. - Designing wake word detection models with ultra-low latency and high accuracy even in noisy conditions. In addition to the above, you will also be involved in: - Architecting bi-directional real-time audio streaming pipelines utilizing WebSocket, gRPC, Twilio Media Streams, or WebRTC. - Integrating voice AI models into live voice agent solutions, IVR automation, and AI contact center platforms. - Building scalable microservices for audio processing, encoding, and streaming across various codecs and containers. - Leveraging deep learning and NLP techniques for speech and language tasks. Furthermore, you will be responsible for: - Developing reusable modules for different voice tasks and system components. - Designing APIs and interfaces for orchestrating voice tasks across multi-stage pipelines. - Writing efficient Python code, optimizing models for real-time inference, and deploying them on cloud platforms. Join us to be part of impactful work, tremendous growth opportunities, and an innovative environment at Tanla, where diversity is championed and inclusivity is valued.,

Join our fast-paced Corporate Oversight and Governance Technology AI/ML team at J.P. Morgan, where your skills and passion will drive innovation and make a significant impact. We offer unparalleled opportunities for career growth and a collaborative environment where you can thrive and contribute to meaningful projects. As an Applied AI ML Lead within the Corporate Oversight and Governance Technology team, you will solve challenging and impactful problems across a wide range of Corporate functions. You will study complex business problems and apply advanced algorithms to develop, test, and evaluate AI/ML applications or models. Working with the firm's rich data pool using Python/Spark via AWS, you will derive business insights and present them to non-technical audiences, contributing to stronger business programs and improved efficiency. Proactively develop an understanding of key business problems and processes. Execute tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Generate structured and meaningful insights from data analysis and modeling exercises and present them in an appropriate format according to the audience. Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions. Carry out ad-hoc and periodic analysis as required by the business stakeholder, model risk function, and other groups. Required Qualifications, Capabilities, and Skills: - Formal training or certification in applied AI/ML concepts and 5+ years of applied experience. - Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API. - Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API. - Experience in building AI/ML models on structured and unstructured data along with model explainability and model monitoring. - Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs. - Experience with a broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, and XGBoost. - Experience with graph analytics and neural networks (PyTorch). - Excellent problem-solving, communication (verbal and written), and teamwork skills. Preferred Qualifications, Capabilities, and Skills: - Expertise in building AI/ML models on structured and unstructured data along with model explainability and model monitoring. - Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG). - Familiarity with the financial services industry.,

You will be joining Firstsource Solutions Limited as a Data Scientist where you will be utilizing your expertise in Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, and Generative AI to develop innovative data-driven solutions and applications. Your role will involve designing and deploying dynamic models and applications using modern web frameworks like Flask and FastAPI, ensuring efficient deployment and ongoing monitoring of these systems. Your responsibilities will include designing and implementing advanced ML and DL models, developing web applications for model deployment to enable real-time data processing and user interaction, performing exploratory data analysis to understand underlying patterns and trends, creating data processing pipelines to prepare large datasets for analysis and modeling, employing Generative AI techniques for content generation, and collaborating with cross-functional teams to integrate AI capabilities into products and systems. To excel in this role, you should have a BE, Masters, or PhD in Computer Science, Data Science, Statistics, or a related field, along with 5 to 7 years of relevant experience in a data science role focusing on ML, DL, and statistical modeling. Strong coding skills in Python, experience with Flask or FastAPI, proficiency in ML and DL frameworks (e.g., PyTorch, TensorFlow), CV (e.g., OpenCV), and NLP libraries (e.g., NLTK, spaCy) are essential. Experience with generative models such as GANs, VAEs, or Transformers, deployment skills with Docker, Kubernetes, and CI/CD pipelines, and excellent analytical and communication skills are also required. Certifications in Data Science, ML, or AI from recognized institutions will be an added advantage. This position is available in multiple locations including Hyderabad, Mumbai, Bangalore, and Chennai. Firstsource follows a fair, transparent, and merit-based hiring process and does not ask for money at any stage. Be cautious of fraudulent offers and always verify through official channels or @firstsource.com email addresses.,

You will be working as a Data Scientist at FSL, utilizing your expertise in Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, and Generative AI to create innovative data-driven solutions and applications. Your role will involve designing and implementing advanced ML and DL models, developing web applications for model deployment using Flask and FastAPI, and ensuring efficient deployment and monitoring of these systems. Additionally, you will be responsible for performing data analysis, including exploratory data analysis to identify patterns and trends, as well as developing data processing pipelines to prepare large datasets for analysis and modeling. Your tasks will also include utilizing Generative AI techniques to create new data points and enhance content generation, collaborating with cross-functional teams to integrate AI capabilities into products, and ensuring that AI solutions align with business goals and user requirements. You will need to stay updated with the latest AI, ML, DL, CV, and NLP developments, explore new technologies, and effectively communicate complex quantitative analysis to senior management and other departments. To qualify for this role, you should have a BE, Masters, or PhD in Computer Science, Data Science, Statistics, or a related field, along with 5 to 7 years of experience in a data science role focusing on ML, DL, and statistical modeling. Your technical skills should include strong coding abilities in Python, experience with Flask or FastAPI, proficiency in ML and DL frameworks (PyTorch, TensorFlow), CV (OpenCV), and NLP libraries (NLTK, spaCy). Experience with generative models like GANs, VAEs, or Transformers, as well as deployment skills with Docker, Kubernetes, and CI/CD pipelines, are also required. Strong analytical skills, excellent communication skills, and certifications in Data Science, ML, or AI from recognized institutions will be advantageous. This position is available in Hyderabad, Mumbai, Bangalore, and Chennai.,

Role & responsibilities Experimenting with new Generative AI models Developing proof of concepts and pilots for various Generative AI and Agentic AI use cases Collaborating with Solution Architects and Enterprise Architects Evangelizing Generative AI and AI adoption within the bank Creating and presenting demos to promote reference architectures Experience: 5+ years of relevant experience Skills: 1. Programming Languages: a. Python 2. Machine Learning Frameworks: a. PyTorch b. TensorFlow 3. Experience with Generative AI Models: a. GANs (Generative Adversarial Networks) b. VAEs (Variational Autoencoders) c. Transformers 4. Cloud Platforms: a. AWS (Amazon Web Services) b. GCP (Google Cloud Platform) c. Azure 5. Data Handling: a. Feature stores b. Vector stores c. Retrieval-Augmented Generation (RAG) d. Data visualization 6. Big Data Technologies: a. Spark 7. Workflow Orchestration: a. Airflow

As a Senior AI/ML Engineer, you will be responsible for designing, developing, and deploying advanced AI models with a focus on generative AI, including transformer architectures such as GPT, BERT, T5, and other deep learning models utilized for text, image, or multimodal generation. You will work with extensive and complex datasets by cleaning, preprocessing, and transforming data to meet quality and relevance standards for generative model training. Additionally, you will collaborate with cross-functional teams to identify project objectives and create solutions using generative AI tailored to business needs. Your role will involve implementing, fine-tuning, and scaling generative AI models in production environments to ensure robust model performance and efficient resource utilization. You will also be responsible for developing pipelines and frameworks for efficient data ingestion, model training, evaluation, and deployment, including A/B testing and monitoring of generative models in production. It is essential to stay informed about the latest advancements in generative AI research, techniques, and tools, applying new findings to improve model performance, usability, and scalability. Furthermore, documenting and communicating technical specifications, algorithms, and project outcomes to technical and non-technical stakeholders with an emphasis on explainability and responsible AI practices. To qualify for this role, you should hold a Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field. A relevant Ph.D. or research experience in generative AI would be advantageous. You should have 12-16 years of experience in machine learning, with at least 8 years in designing and implementing generative AI models or working specifically with transformer-based models. The ideal candidate will possess expertise in generative AI, transformer models, GANs, VAEs, text generation, and image generation. Strong knowledge of machine learning algorithms, deep learning, neural networks, and programming skills in Python and SQL are required. Familiarity with libraries such as Hugging Face Transformers, PyTorch, TensorFlow, and experience with MLOps tools like Docker, Kubernetes, MLflow, and Cloud Platforms (AWS, GCP, Azure) is essential. Additionally, proficiency in data engineering concepts such as data preprocessing, feature engineering, and data cleaning is preferred. Joining our team will provide you with an opportunity to work on technical challenges with global impact, vast opportunities for self-development through online university access and sponsored certifications, sponsored Tech Talks & Hackathons, and a generous benefits package including health insurance, retirement benefits, flexible work hours, and more. You will work in a supportive environment with forums to explore passions beyond work, offering an exciting opportunity to contribute to cutting-edge solutions while advancing your career in a dynamic and collaborative setting.,

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.,

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

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)

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.