54 Kubeflow Jobs - Page 3

Job Summary Director, Collection Platforms and AI As a director, you will be essential to drive customer satisfaction by delivering tangible business results to the customers. You will be working for the Enterprise Data Organization and will be an advocate and problem solver for the customers in your portfolio as part of the Collection Platforms and AI team. You will be using communication and problem-solving skills to support the customer on their automation journey with emerging automation tools to build and deliver end to end automation solutions for them. Team Collection Platforms and AI Enterprise Data Organizations objective is to drive growth across S&P divisions, enhance speed and productivity in our operations, and prepare our data estate for the future, benefiting our customers. Therefore, automation represents a massive opportunity to improve quality and efficiency, to expand into new markets and products, and to create customer and shareholder value. Agentic automation is the next frontier in intelligent process evolution, combining AI agents, orchestration layers, and cloud-native infrastructure to enable autonomous decision-making and task execution. To leverage the advancements in automation tools, its imperative to not only invest in the technologies but also democratize them, build literacy, and empower the work force. The Collection Platforms and AI team's mission is to drive this automation strategy across S&P Global and help create a truly digital workplace. We are responsible for creating, planning, and delivering transformational projects for the company using state of the art technologies and data science methods, developed either in house or in partnership with vendors. We are transforming the way we are collecting the essential intelligence our customers need to do decision with conviction, delivering it faster and at scale while maintaining the highest quality standards. What were looking for ? You will lead the design, development, and scaling of AI-driven agentic pipelines to transform workflows across S&P Global. This role requires a strategic leader who can architect end-to-end automation solutions using agentic frameworks, cloud infrastructure, and orchestration tools while managing senior stakeholders and driving adoption at scale. A visionary technical leader with knowledge of designing agentic pipelines and deploying AI applications in production environments. Understanding of cloud infrastructure (AWS/Azure/GCP), orchestration tools (e.g., Airflow, Kubeflow), and agentic frameworks (e.g., LangChain, AutoGen). Proven ability to translate business workflows into automation solutions, with emphasis on financial/data services use cases. An independent proactive person who is innovative, adaptable, creative, and detailed-oriented with high energy and a positive attitude. Exceptional skills in listening to clients, articulating ideas, and complex information in a clear and concise manner. Proven record of creating and maintaining strong relationships with senior members of client organizations, addressing their needs, and maintaining a high level of client satisfaction. Ability to understand what the right solution is for all type of problems, understanding and identifying the ultimate value of each project. Operationalize this technology across S&P Global, delivering scalable solutions that enhance efficiency, reduce latency, and unlock new capabilities for internal and external clients. Exceptional communication skills with experience presenting to C-level executives Responsibilities Engage with the multiple client areas (external and internal) and truly understand their problem and then deliver and support solutions that fit their needs. Understand the existing S&P Global product to leverage existing products as necessary to deliver a seamless end to end solution to the client. Evangelize agentic capabilities through workshops, demos, and executive briefings. Educate and spread awareness within the external client-base about automation capabilities to increase usage and idea generation. Increase automation adoption by focusing on distinct users and distinct processes. Deliver exceptional communication to multiple layers of management for the client. Provide automation training, coaching, and assistance specific to a users role. Demonstrate strong working knowledge of automation features to meet evolving client needs. Extensive knowledge and literacy of the suite of products and services offered through ongoing enhancements, and new offerings and how they fulfill customer needs. Establish monitoring frameworks for agent performance, drift detection, and self-healing mechanisms. Develop governance models for ethical AI agent deployment and compliance. Preferred Qualification 12+ years work experience with 5+ years in the Automation/AI space Knowledge of: Cloud platforms (AWS SageMaker, Azure ML; etc) Orchestration tools (Prefect, Airflow; etc) Agentic toolkits (LangChain, LlamaIndex, AutoGen) Experience in productionizing AI applications. Strong programming skills in python and common AI frameworks Experience with multi-modal LLMs and integrating vision and text for autonomous agents. Excellent written and oral communication in English Excellent presentation skills with a high degree of comfort speaking with senior executives, IT Management, and developers. Hands-on ability to build quick prototype/visuals to assist with high level product concepts and capabilities. Experience in deployment and management of applications utilizing cloud-based infrastructure. A desire to work in a fast-paced and challenging work environment Ability to work in a cross functional, multi geographic teams

Hi, We are hiring for ML Developer. Please find the attached JD. Key Responsibilities Design, develop, and optimize machine learning models for various business applications. Build and maintain scalable AI feature pipelines for efficient data processing and model training. Develop robust data ingestion, transformation, and storage solutions for big data. Implement and optimize ML workflows, ensuring scalability and efficiency. Monitor and maintain deployed models, ensuring performance, reliability, and retraining when necessary Preferred candidate profile Qualifications and Experience Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3.5 to 5 years of experience in machine learning, deep learning, or data science roles. Proficiency in Python and ML frameworks/tools such as PyTorch, Langchain Experience with data processing frameworks like Spark, Dask, Airflow and Dagster Hands-on experience with cloud platforms (AWS, GCP, Azure) and ML services. Experience with MLOps tools like ML flow, Kubeflow Familiarity with containerisation and orchestration tools like Docker and Kubernetes. Excellent problem-solving skills and ability to work in a fast-paced environment. Strong communication and collaboration skills.

Job description Job description Job Summary: We are looking for a results-driven and innovative Data Scientist with 46 years of experience in data analysis, machine learning, and product optimization. The ideal candidate will have a strong foundation in Python, SQL, and cloud services, along with practical exposure to GenAI, LLMs, and MLOps frameworks. You will be responsible for building scalable data pipelines, developing machine learning models, and solving real-world business problems with data-driven solutions. Key Responsibilities: Design and deploy LLM-powered solutions (e.g., RAG, LangChain, Vector DB) to enhance business processes. Build and fine-tune traditional ML models (Random Forest, Decision Trees) for predictive analytics. Optimize LLM performance using LoRA fine-tuning and post-training quantization. Develop and deploy containerized AI applications using Docker and FastAPI. Collaborate with agents like CrewAI and LangSmith to automate document processing and data extraction workflows. Implement Python & SQL-based ETL pipelines for real-time data ingestion. Design dashboards and KPI monitoring tools using Metabase to enable data-driven decision-making. Create data consumption triggers and automate reporting for international stakeholders. Moderate large-scale live virtual data science classes and provide operational support. Required Skills: Programming: Python, SQL, FastAPI ML & AI: Random Forest, Decision Trees, Clustering, PCA, DL, NLP, Transformers, Gen AI Frameworks/Tools: LangChain, MLflow, Kubeflow, CrewAI, LangSmith DevOps: Docker, Git Databases: MySQL, PostgreSQL, MongoDB Cloud: AWS (S3, EC2) Visualization: Metabase Other: Experience with LLM fine-tuning and quantization Role & responsibilitiesRole & responsibilities Preferred candidate profile

Role & responsibilities Develop, improve, and expand deployment pipeline infrastructure and services Build and deploy hyperscale machine learning pipelines into production Works closely with the developer teams to provide a self-service test integration, test coverage and quality Collaborate with the Security Engineering and Architecture teams to ensure code meets security requirements and is deployed in alignment with standards and governance processes Debug and resolve the issues developers encounter when attempting to use cloud services, with an eye toward continuously improving developer experience Execute against a vision of continuous integration and delivery that empowers developers Preferred candidate profile Building Infrastructure-as-code for Public Cloud using Terraform Experience in a Dev Ops engineering or equivalent role. Experience developing, enhancing, and maintaining CI/CD automation and configuration management using tools such as Jenkins, Snyk, and GitHub Experience with MLOps and orchestration tools such as Airflow, Kubeflow, Optuna, Mlflow or other similar MLOps tools. Experience with operationalizing and migrating ML models into production at scale Experience developing large scale model inference solutions using a parallel execution framework using Spark, EMR, or Databricks Experience developing complex orchestration and MLOps pipelines stitching together large volumes of data for training and scoring Proficiency with Apache Spark, EMR/DataProc and Cloud based tools (Snowflake, Redshift, EMR, Glue, Step Functions, Lambda, Step functions, AWS Batch, or similar etc.) Experience with ML libraries like H20, scikit learn and deep learning frameworks (PyTorch, TensorFlow, etc.) Familiarity with large scale production systems and technologies; for example load balancing, monitoring, distributed systems, and configuration management Development skills with at least one of the following programming languages: Python (preferred), Node.js, Bash. Knowledge of testing automation and code scanning tools Knowledge and experience with running distributed systems in public cloud environments such as AWS, Azure, and GCP. Knowledge of container orchestration platforms such as Kubernetes Knowledge of CDNs, DNS, web servers, and other supporting Internet services Source code management tools using Git. Experienced with standard Gitflow workflow Familiar with best practices for infrastructure security, reliability, and fault tolerance Passionate about automating and improving processes