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AI / MLOps Engineer
Builds and deploys AI/ML models from prototype to production, focusing on MLOps pipelines, model monitoring, and scalable infrastructure for public-sector and energy clients using PyTorch, TensorFlow, and cloud platforms like AWS SageMaker.
Data Science & AI Engineer
Build and deploy AI models (computer vision, ML) to turn operational data into insights for avionics and manufacturing, using Python, TensorFlow/PyTorch, and MLOps.
Lead Engineer/ Engineer, DevOps & MLOps (AI Platform), xCloud
Build and maintain the AI platform, automate ML workload deployments, and engineer secure DevSecOps pipelines using Kubernetes, GitLab, and ML tools.
Lead Engineer/ Engineer, DevOps & MLOps (AI Platform), xCloud
Build and maintain the AI platform’s DevOps/MLOps infrastructure, automate CI/CD pipelines, and deploy scalable ML workloads using Kubernetes, Kubeflow, and GitLab.
Architect DevOps & MLOps for AI Platform
Design and maintain scalable ML infrastructure and automated pipelines for an AI platform in homeland security, using tools like Kubeflow, MLflow, and ArgoCD.
Principal Cloud Architect
Principal Cloud Architect designs scalable cloud infrastructure, data platforms, and AI-augmented review workflows, prototypes patterns, and hands them off to engineering teams.
Devops Engineer, Surveillance
Build and maintain scalable cloud infrastructure and CI/CD pipelines for surveillance analytics, container orchestration, and ML workloads at a global alternative investment firm.
Machine Learning Infrastructure Engineer, GenAI Technology
Designs and maintains high-performance infrastructure for large-scale generative AI and machine learning workloads, including distributed systems, GPU compute, and MLOps pipelines in a financial firm.
Key Customers Solutions Architect
Designs and deploys large-scale AI/ML workloads on GPU cloud infrastructure, advising key customers and optimizing performance while collaborating with sales and product teams.
MLOps
Build and run a production-grade ML platform: design AI system architectures, deploy and optimize LLM inference servers, and maintain MLOps pipelines with GPU scheduling and monitoring.
DevOps/MLOps Specialist
Education and Work Experience Requirements: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.5+ years of experience in MLOps with at least 2 years working specifically…
Data Scientist
Build and deploy ML models (regression, deep learning, LLMs) to power hotel-commerce features like demand forecasting and recommendations using Python, PySpark, and Databricks.
Senior Data Scientist
Senior Data Scientist builds ML models (XGBoost, time-series, recommendations) end-to-end, engineers features from terabyte-scale data, and deploys solutions in production for a top-3 Russian digital bank.
Senior Member Technical Staff (MTS 3) - Machine learning
Lead AI/ML development for product lines, building and deploying models from data processing to production, including LLMs, while mentoring a team of data scientists.
AI/ML Engineer
Build production-grade AI architecture blueprints and open-source Quickstarts with Python, PyTorch, and Kubernetes, focusing on enterprise deployment, security, and regulated environments.
Senior Machine Learning Engineer
Build and deploy ML models and LLM agents for beauty ecommerce, including recommendation engines and real-time APIs using PyTorch, FastAPI, and GCP VertexAI.
AI Solutions Engineer, HKD 70K - 95K x 12 + Bonus
Build production-grade AI tools and RAG pipelines for a global enterprise, integrating generative models into executive decision-making workflows using Python, cloud infra, and MLOps.
Lead GCP MLOps Engineer
Lead the design and automation of GCP-based MLOps pipelines to deploy, monitor, and scale Python ML models using Vertex AI, CI/CD, and Terraform.
Machine Learning Modeling Lead - Credit Modeling
Lead the design and deployment of ML models for credit risk, collections, fraud detection, and customer segmentation in digital lending, using Python, SQL, and MLOps tools.
AI Engineer
Build and deploy AI models and infrastructure to accelerate life-science research, bridging cutting-edge AI with experimental workflows at EMBL’s Heidelberg hub.