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Onsite Applied AI Engineer in Los Angeles building end-to-end AI features (LLM, computer vision) from conception to deployment using Python, TensorFlow, Django, Flask, and AWS.
An Applied AI Scientist designing, developing, and deploying machine learning models (NLP, TensorFlow/PyTorch) in cloud environments using Python and Docker, in a hybrid role based in Acalanes Ridge, California.
Data Engineer building and maintaining scalable ETL data pipelines for AI/ML model training, using Python, SQL, and tools like Airflow in an onsite role in Pune.
Machine Learning Engineer implementing ML solutions on a Procure to Pay project in a hybrid Pune setting, writing clean Python/ML code under senior mentorship with a focus on data preprocessing, model building, and team collaboration.
An AI Research Engineer develops and deploys machine learning models, conducts research, and collaborates with teams to solve complex problems using Python, TensorFlow, PyTorch, and NLP/Computer Vision techniques.
Build and deploy ML models in AWS/Azure for healthcare analytics, refactoring data scientists' code into production batch jobs and optimizing cloud costs.
The Data Scientist will develop and integrate statistical and machine learning models, including LLMs and AI agents, into core insurance and healthcare business processes. The role involves end-to-end data engineering, model development, and communicating insights to management.
The Data Scientist will design and deploy machine learning models and predictive frameworks to optimize business value within Hitachi Energy's Transformers business unit. The role involves collaborating with cross-functional teams to build robust data pipelines using Azure, Databricks, and Python while ensuring model transparency and performance.
The Data Scientist will design, develop, and deploy scalable machine learning and AI solutions, focusing on Generative AI, LLMs, and NLP models. The role involves end-to-end ownership of projects, from data analysis and feature engineering to production deployment and monitoring using Python and PySpark.
Machine Learning Engineer designing distributed backend systems, deploying production ML models, and building scalable data pipelines using Java, Python, Apache Kafka, and Spark at DEKA Research & Development.
This Data Scientist role supports a FinCEN program in Washington, DC, by applying machine learning and statistical modeling to large-scale financial datasets to detect financial crimes. The position requires an active Top Secret/SCI clearance and proficiency in Python, R, SQL, and AWS cloud-native technologies.
The AI Engineer will design, develop, and deploy AI-powered solutions, focusing on Generative AI, LLMs, and MLOps. The role involves building scalable applications using Python and integrating them into enterprise systems via APIs.
Build and deploy data models to analyze credit card portfolios, customer behavior, and transaction trends, then translate insights into actionable strategies for Kotak Mahindra Bank’s financial services.
The Applied AI ML Associate develops and manages fraud and credit risk models for JPMorgan Chase's digital bank. The role involves end-to-end model lifecycle management, performance monitoring, and regulatory compliance using Python and machine learning frameworks.
Role: Databricks Engineer Experience: 9-12 Years Location: ALL EXL Locations Work Mode: Hybrid Key Role and Responsibilities: Design, build, and maintain Databricks workspaces, clusters, and compute pools across…
Builds predictive models and data products to drive customer lifetime value (CLV) by developing churn/retention models, segmentation strategies, and personalized CRM campaigns. Owns end-to-end ML workflows, including feature engineering, validation, deployment, and production monitoring in Databricks, while collaborating with CRM, Marketing, and Merchandising teams.
Build and deploy predictive models and statistical analyses to detect tax fraud and non-compliance using Python/R, SQL, and Databricks for the IRS.
Develops advanced AI/ML models (tree models, deep learning, reinforcement learning, Generative/Agentic AI) to optimize P&G’s billion-dollar operations, combining cloud-based data analysis with scalable production deployment.
Data Scientist at P&G designing and implementing scalable ML/AI algorithms—including Generative/Agentic AI, optimization, and deep learning—on massive cloud-hosted datasets using Python and mainstream ML libraries.
Design and build enterprise ontologies, knowledge graphs, and semantic data models using W3C standards (OWL, RDF, SPARQL) and graph databases to connect raw operational data with AI-ready intelligence for defense programs at RTX.
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