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Build and maintain scalable data pipelines and infrastructure using Databricks, Spark, ClickHouse, and Kafka to power real-time analytics and ML for mobile app monetization.
Build and maintain scalable data pipelines and production-grade ML systems for clinical trials, pharmacovigilance, and drug manufacturing at a global pharma company.
Build and run the AIOps platform that keeps AI models, LLM pipelines, and agents reliable, scalable, and cost-efficient in AWS/Azure.
Build and maintain data pipelines and warehouses for AI projects, using PySpark, Databricks, and Azure services to centralize and process large datasets.
Build and maintain SeQura’s data pipelines and infrastructure, transforming raw payment data into reliable analytics tools for teams across the fintech.
Build and deploy AI systems end-to-end, from prototypes to production, using Python and ML frameworks while collaborating with cross-functional teams.
Builds internal tools and data dashboards using React/Next.js/TypeScript on the frontend and Python (FastAPI/Django) on the backend for a quantum AI company.
Build and deploy production-grade AI systems end-to-end, from data pipelines and ML models to scalable deployment and monitoring using Python, PyTorch, and MLOps tools.
Build and ship full-stack features with JavaScript/TypeScript (Node, React) in a cross-functional team, iterating from concept to polished product.
Build and maintain web and mobile apps using JavaScript/React/Node.js and databases; collaborate with cross-functional teams to ship features and debug issues.
Designs and maintains Azure-based Kubernetes clusters and CI/CD pipelines using Terraform, Jenkins, and Docker for a product company.
Build and run the cloud platform that powers the company’s AI services on AWS and Kubernetes, automating deployments with Terraform and CI/CD while supporting MLOps workflows like SageMaker, MLflow, Airflow and Kubeflow.
Build and maintain cloud/hybrid CI/CD pipelines, Kubernetes clusters, and automation toolchains for Siemens Mobility’s transport-focused SaaS platforms.
Build and maintain cloud infrastructure using Azure, Kubernetes, Terraform, and CI/CD pipelines with Jenkins and Azure DevOps.
Design and manage scalable cloud infrastructure for AI workloads using AWS, Kubernetes, Terraform, and observability tools.
Build and deploy AI-driven data analytics and generative AI solutions on Azure and GCP, including MLOps pipelines and agentic AI workflows.
Build and maintain ML models for an energy-data platform that collects sensor and consumption data from buildings to help owners cut costs and emissions.
Design and maintain AI-driven data pipelines in Python, ensuring efficient processing and compliance while translating business needs into data solutions.
Build and fine-tune large language models for Avito’s products, optimizing training pipelines and inference speed for production-scale NLP systems.
Build and maintain AI-powered DevOps pipelines for an internal AI agent ecosystem, focusing on LLM inference, MCP integration, and AI artifact tracking in software releases.
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