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An MLOps Engineer leads technical initiatives, designs scalable ML systems, and mentors teams to implement end-to-end ML workflows using AWS/Azure, Kubernetes, and MLOps tools like MLflow and Kubeflow.
The ML Engineer integrates AI/ML models into production, optimizes inference pipelines, manages CI/CD workflows, and builds Agentic/multimodal AI systems using Python, PyTorch/TensorFlow, Docker, Kubernetes, and cloud platforms.
Builds and deploys generative AI and agentic systems using LLMs, LangChain/LangGraph, and Python to automate workflows and support healthcare applications.
Senior MLOps Engineer in Singapore builds and maintains AWS-based pipelines to deploy, scale, and monitor machine learning models for manufacturing and semiconductor analytics, focusing on GenAI and classical ML workflows.
Staff Engineer leading architecture, design, and hands-on development of enterprise AI/GenAI solutions—LLMs, RAG, and agentic workflows—using Python, FastAPI, LangChain/LangGraph, and Azure OpenAI at a global life-sciences company.
Builds production-grade AI/Generative AI solutions (LLMs, RAG, agentic workflows) for scientific/healthcare systems, integrating models into backend services and cloud platforms.
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.
As a Data Scientist at enercity, you will develop machine learning and time series forecasting models for energy prices and volumes using Python and Azure. You will manage the end-to-end data pipeline, from data ingestion and transformation using Snowflake and dbt to deploying models and visualizing insights for trading and management stakeholders.
The Senior Machine Learning Engineer will design, build, and operate production-grade AI systems, specifically RAG applications and Intelligent Document Processing pipelines, for federal clients using AWS GovCloud. The role requires deep expertise in LLMs, vector databases, and secure, compliant software engineering practices.
The Senior Machine Learning Engineer will design and build production-grade Intelligent Document Processing (IDP) and AI systems using Python, NLP, OCR, and LLMs. The role involves managing the full AI lifecycle, from data ingestion and model integration to deployment and observability in cloud environments.
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.
At Signature Aviation, we are modernizing operations and customer experiences through advanced data platforms and artificial intelligence. We are seeking a Principal Engineer – AI Agentic Systems to design, architect,…
Lead the design and development of full-stack applications and AI-driven infrastructure platforms using Java, Python, Node.js, and modern frameworks, while automating CI/CD, DevOps, and cloud-native systems.
The Data Governance & AI Consultant will deliver data governance frameworks, policies, and AI assurance strategies for UK Central Government and Defence projects. The role involves managing data quality, metadata, and responsible AI practices while working in a hybrid capacity in Bristol.
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.
Leads DevOps for analytics/data/AI platforms, ensuring scalable, secure, and cost-effective cloud operations with CI/CD, IaC, and SRE practices.
Develops AI/ML solutions (predictive models, generative AI, agentic systems) for government case management modernization, focusing on compliance, MLOps, and data-driven decision tools.
Lead the design and productionization of AI/ML capabilities—including generative AI, forecasting, and recommendation systems—building data pipelines and production platforms using Python, SQL, Snowflake/Databricks, and Azure.
Lead Data Scientist translates business challenges into data-driven insights by building, deploying, and optimizing ML models (including generative AI) using Python/R/Scala. Focuses on full AI workflows—from feature engineering to model monitoring—while mentoring teams and advising executives on strategic decisions.
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