... LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production. • Direct the development and deployment of ...
... LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production. • Direct the development and deployment of ...
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
Llmops information
What jobs can I do with LLM?
With expertise in large language models (LLMs), you can pursue roles such as NLP engineer, machine learning engineer, data scientist, or AI researcher. These jobs typically require skills in programming, deep learning frameworks, and understanding of natural language processing concepts.
What engineer makes $500,000 a year?
Senior machine learning engineers, including those working in Llmops or related AI fields, can earn $500,000 or more annually, especially with extensive experience, specialized skills, and working at top tech companies or in high-demand industries. Compensation often includes base salary, bonuses, and stock options, reflecting expertise in large language models, cloud platforms, and deployment tools.
What jobs make $3,000 a day?
In the context of Llmops, high-paying roles such as AI project managers, machine learning engineers, or data science consultants can earn around $3,000 daily, especially with specialized skills, certifications, and experience in deploying large language models. These roles often require advanced technical knowledge, experience with cloud platforms, and the ability to manage complex AI operations in a fast-paced environment.
What is the difference between Llmops vs Data Scientist?
| Aspect | Llmops | Data Scientist |
|---|---|---|
| Required credentials | Knowledge of machine learning, AI frameworks, cloud platforms | Statistics, programming, data analysis skills |
| Work environment | AI/ML teams, cloud environments, deployment pipelines | Data analysis, modeling, reporting in various industries |
| Employer usage | Tech companies, AI startups, research labs | Finance, healthcare, tech, retail |
While both roles involve working with data and machine learning, Llmops focuses on deploying and maintaining large language models in production environments, requiring expertise in AI infrastructure. Data Scientists primarily analyze data, build models, and generate insights. Llmops professionals ensure models operate efficiently at scale, whereas Data Scientists develop the models and interpret results.
What is a $900000 AI job?
A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in deep learning, data science, and programming. These roles usually involve leadership, strategic planning, and expertise with tools like TensorFlow or PyTorch, and may include performance-based bonuses or stock options. Such salaries are rare and generally found in top tech companies or specialized AI firms.
What are popular job titles related to Llmops jobs in Delaware? For Llmops jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Llmops jobs in Delaware look for? The top searched job categories for Llmops jobs in Delaware are:
What cities in Delaware are hiring for Llmops jobs? Cities in Delaware with the most Llmops job openings:
Full-time
Re-posted 3 days ago
Job description
Job Summary:
JPMorgan Chase is one of the oldest financial institutions, providing innovative financial solutions to a diverse clientele. The Applied AI/ML Lead will drive machine learning and generative AI projects, working collaboratively with product managers and engineers to implement cutting-edge AI solutions that enhance the Home Lending sector.
Responsibilities:
• Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
• Design, develop, and deploy state-of-the-art AI/ML/GenAI solutions to meet business objectives.
• Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
• Direct the development and deployment of advanced generative AI solutions (LLMs, RAG, NLP, AI Agents) and classical ML models, integrating state-of-the-art techniques into the ML platform to create innovative fintech products.
• Develop advanced monitoring and management tools to ensure high reliability and scalability of AI/ML systems.
• Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
• Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
• Communicate AI/ML capabilities and results to both technical and non-technical audiences.
• Build AI Agents and chatbot
• Stay informed about the latest trends and advancements in the latest AI/ML research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
Qualifications:
Required:
• Bachelor’s degree or MS or PhD in quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science.
• 5+ years of experience in Machine Learning and Artificial Intelligence engineering.
• Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
• Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
• Extensive hands-on technical experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, AWS Bedrock, Transformers, LangChain/LngGraph.
• Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), orchestration tools (Airflow, FastAPI, etc.) and architectural design, implementation, and performance optimization.
• Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, deep learning, reinforcement learning), and generative model architectures.
• Expert in Large Language models (OpenAI, Anthropic, Mistral, etc) including fine-tuning models, prompt engineering, embeddings and context window.
• Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
Preferred:
• Familiarity with the financial services industries.
• Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
• Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
• Familiarity with ethical AI, including bias mitigation, explainability and escalation protocols for risky outputs.
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.
JPMorgan Chase is one of the oldest financial institutions, providing innovative financial solutions to a diverse clientele. The Applied AI/ML Lead will drive machine learning and generative AI projects, working collaboratively with product managers and engineers to implement cutting-edge AI solutions that enhance the Home Lending sector.
Responsibilities:
• Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
• Design, develop, and deploy state-of-the-art AI/ML/GenAI solutions to meet business objectives.
• Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
• Direct the development and deployment of advanced generative AI solutions (LLMs, RAG, NLP, AI Agents) and classical ML models, integrating state-of-the-art techniques into the ML platform to create innovative fintech products.
• Develop advanced monitoring and management tools to ensure high reliability and scalability of AI/ML systems.
• Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
• Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
• Communicate AI/ML capabilities and results to both technical and non-technical audiences.
• Build AI Agents and chatbot
• Stay informed about the latest trends and advancements in the latest AI/ML research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
Qualifications:
Required:
• Bachelor’s degree or MS or PhD in quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science.
• 5+ years of experience in Machine Learning and Artificial Intelligence engineering.
• Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
• Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
• Extensive hands-on technical experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, AWS Bedrock, Transformers, LangChain/LngGraph.
• Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), orchestration tools (Airflow, FastAPI, etc.) and architectural design, implementation, and performance optimization.
• Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, deep learning, reinforcement learning), and generative model architectures.
• Expert in Large Language models (OpenAI, Anthropic, Mistral, etc) including fine-tuning models, prompt engineering, embeddings and context window.
• Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
Preferred:
• Familiarity with the financial services industries.
• Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
• Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
• Familiarity with ethical AI, including bias mitigation, explainability and escalation protocols for risky outputs.
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.