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Python Developer
Builds Python-based data pipelines and ML environments on GCP, using FastAPI/Flask and Kubeflow to process TV viewership analytics and automate Data Science workflows.
Montréal [Hybrid] - DevOps - MLOps engineer
Build and maintain cloud infrastructure for AI models, automate deployments with CI/CD, and optimize GPU-powered production environments using DevOps/MLOps tools like Terraform, Kubernetes, and MLflow.
Cloud Platform DevOps Engineer - Assistant Vice President
Designs, builds, and maintains secure, scalable cloud infrastructure for AI/ML platforms and DevOps tooling, using Kubernetes, Helm, and CI/CD pipelines while mentoring teams.
Python Developer – (AI Applications)
Build and deploy enterprise AI services using Python, Flask/FastAPI, and cloud-native tools to power UKG’s next-gen AI platform.
Senior Python Developer
Build and maintain the ML platform that powers Prima’s motor-insurance pricing and claims systems using Python, cloud tools, and CI/CD pipelines.
MLOps Azure DevOps Engineer
Build and maintain scalable ML pipelines on Azure, automating training, deployment, and monitoring with DevOps practices and Kubernetes.
AI/ML Architect (Remote)
Design enterprise AI/ML pipelines and governance frameworks for the VA, ensuring responsible deployment of healthcare and benefits systems that meet federal standards.
Applied AI ML Lead - DocAI
Lead AI/ML engineering at J.P. Morgan, building NLP or computer-vision models to automate financial processes and improve business decisions using PyTorch, distributed frameworks, and MLOps tooling.
AI Software Architect
Design and deploy AI-powered features (LLM agents, RAG, NLP/vision) into a Python-based property-management platform, integrating FastAPI/Django with modern AI frameworks and ensuring secure, scalable production systems.
Applied AI ML - Senior Associate - Machine Learning Engineer
Build and deploy AI/ML models (NLP, computer vision) for J.P. Morgan’s Commercial & Investment Bank, combining research with production engineering to automate decisions and processes.
Lead AI Engineer - London
Lead AI Engineer designs, builds, and deploys enterprise-scale AI/ML and Generative AI systems, including RAG pipelines and agentic workflows, across Azure, GCP, or AWS.
Fullstack-инженер (middle+ / junior-senior) (FastAPI + React)
Fullstack engineer builds and refactors fintech services using Python FastAPI backend and React frontend, migrating legacy Java systems and shaping new financial products.
AI Engineer (LLM)
Build and maintain production-grade LLM pipelines and multi-agent systems for generative AI features, primarily in finance, using Python and APIs from providers like OpenAI and Anthropic.
Remote DevOps/MLOps Engineer
Designs and maintains CI/CD pipelines, Kubernetes clusters, and cloud infrastructure for ML and general apps, automating deployments and monitoring systems.
Senior Staff Software Engineer, Cloud AI Infrastructure
Lead a cloud engineering team to design and run GCP-based AI infrastructure that ingests robotics data, trains models, and serves inference at scale for BrainOS, the platform powering 30,000+ autonomous mobile robots.
Data Engineer GCP H/F
Build and maintain GCP-based data pipelines for clients, focusing on BigQuery ingestion, transformation, and event-driven architectures using Pub/Sub, Dataflow, and Eventarc.
R&D Data Engineer
Build and deploy MLOps pipelines to collect robotics data, orchestrate model training, and automate deployment for AI-driven warehouse automation systems using Python, cloud infra, and Kubernetes.
Lead Data Engineer
Lead a team to design and build scalable data pipelines and platforms for clients, advising on cloud and big-data tech while enforcing DevOps, FinOps, and governance best practices.
Ingénieur DevOps/Ingénieure DevOps
Build and maintain CI/CD pipelines and Kubernetes-based infrastructure to automate training and deployment of AI/ML models, using Docker, Terraform, Ansible, Jenkins, and monitoring tools like Prometheus and Grafana.
Platform Engineer (DevOps / MLOps Focus)
Build and maintain cloud-native infrastructure and Kubernetes platforms to support large-scale AI and ML workloads using Terraform, CI/CD, and observability tools.