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Build and deploy enterprise-grade AI and GenAI services using Python, FastAPI, and MLOps tools to power Conga’s commercial software products.
Build and maintain enterprise AI and GenAI products using Python, FastAPI, and cloud-native tools; contribute to MLOps pipelines and model deployment for Conga’s commercial operations platform.
A career that’s the whole package! At Conga, we’ve built a community where our colleagues can thrive. Here you’ll find opportunities to innovate and support growth through individual and team development, all within an…
Designs and maintains on-premise Kubernetes infrastructure for AI defense systems, focusing on observability, security, and MLOps platforms to support AI model deployment and monitoring in high-security environments.
The Senior Forward Deployed Solution Engineer will work directly with customers to design, deploy, and troubleshoot complex infrastructure architectures involving Kubernetes, AI/GPU systems, and cloud-native environments. This hands-on role requires deep technical expertise in automation and systems engineering to ensure successful customer outcomes and production readiness.
Senior Data Scientist bridging ML research and production, owning end-to-end pipelines, observability, and platform reliability for LLM/agent systems at an AI-first software company.
Cloud and AI Security Engineer at Axos Bank in Manila, building security frameworks for AI/ML platforms, LLM applications, and multi-cloud infrastructure (AWS, Azure, GCP), focusing on AI-specific threat mitigation like prompt injection, adversarial attacks, and RAG security.
Lead development of scalable AI systems processing engineering documents (CAD/BIM, reports) using Python backends and React frontends, integrating LLMs and deploying on cloud.
Build and maintain MLOps infrastructure for freight-intelligence models, automating monitoring, retraining, and deployment at scale in a logistics-focused product team.
Build and scale secure AWS infrastructure (EKS, Lambda, CI/CD) to run production AI/ML workloads, own observability, and drive platform reliability for Zscaler’s AI-forward security products.
Design and maintain scalable data pipelines and infrastructure to process complex chemical data (chromatograms) for ML model training and deployment in a healthcare/climatetech context.
Designs and maintains data infrastructure for federal government clients, building scalable pipelines and integrating ML models using Python, AWS, and tools like Apache Airflow and Spark.
About Tekion: Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the…
Design, build, and deploy production-ready AI agents and agentic workflows on AWS using LangGraph/CrewAI, RAG pipelines, and Model Context Protocol as part of nearshore software development PODs.
Senior Technical Architect designing and supporting enterprise AI/ML deployments on the Snowflake native stack, performing root cause analysis across distributed cloud systems, and mentoring teams on MLOps best practices.
BURNT Member of Technical Staff AI/ML Engineer (MLOps-Focused) Location On-site, San Francisco · Experience 5–7 years · Compensation $150,000–$275,000 + equity About Burnt Burnt isn't building software on top of…
Designs and maintains PepsiCo’s Kubeflow-based MLOps platform on Azure, ensuring scalable, secure, and cost-effective ML workflows with minimal disruption.
The AI Platform & Machine Learning Engineer will build and maintain scalable infrastructure for training, deploying, and monitoring machine learning models for wearable construction technology. The role focuses on the end-to-end ML lifecycle, utilizing tools like Python, PyTorch, and containerization to bridge the gap between research and production.
Builds and maintains ML infrastructure (training, serving, CI/CD, observability) for AI/ML models and LLMs, bridging research and production teams to deploy scalable, reliable systems.
Machine Learning Engineer (hybrid, Warsaw) building production-grade Python ML applications, MLOps pipelines on GCP/Azure, model-serving systems, and GenAI/LLM deployments for Harvey Nash Technology's clients.
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