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Build and deploy production ML and GenAI components—models, RAG systems, agents, and pipelines—using Python, SQL, and cloud services to power Amgen’s healthcare-focused AI products.
Senior ML engineer building and scaling enterprise-grade AI/ML and GenAI platforms, including MLOps pipelines, model services, and full-stack applications for Amgen’s healthcare data needs.
Build and scale secure AWS infrastructure (EKS, Lambda, Terraform) for AI/ML workloads, automate CI/CD with GitLab, and run a centralized observability stack (Prometheus/Grafana) to support production LLM inference and vector databases.
Senior Staff Software Engineer, Cloud AI Infrastructure Location: Remote, USA Department: Software Engineering Brain Corp is a San Diego, California, USA-based AI company creating transformative core technology for the…
Designs and manages AI-driven robotics automation pipelines (human-in-the-loop workflows) for data collection/annotation, optimizing AI/ML orchestration tools like Airflow or LangChain to integrate GenAI models and APIs for scalable, high-quality solutions.
Build and optimize AI-driven robotics automation pipelines, integrating GenAI models and human-in-the-loop workflows to collect and annotate egocentric data for deployment.
Lead a team of ML engineers and architects at an AI-first cloud services company, owning hiring, team growth, and complex customer engagements while shaping AI/ML architectures and driving pre-sales.
Lead MLOps infrastructure to build, deploy, and monitor production-grade ML models at scale for a biopharma company, integrating generative AI while ensuring regulatory compliance.
Build and ship production-grade AI features (LLM integrations, RAG, agents) for an iGaming affiliate platform, turning prototypes into scalable, high-load services.
Build and deploy ML models and MLOps pipelines using Python, TensorFlow/PyTorch, and cloud platforms like Azure Databricks; work with event-driven architectures and real-time data flows.
Build and maintain cloud and on-premise infrastructure for AI systems that power Singapore’s transport network, including GPU compute, model serving, and MLOps pipelines.
Design and build ML models and real-time data pipelines using Python, TensorFlow/PyTorch, Kafka/PubSub, and Databricks/Fabric for enterprise clients across industries.
Build and operate MLOps pipelines for enterprise AI that runs on-prem and air-gapped factory floors, handling model training, versioning, serving, monitoring, and safe rollouts across GPU and edge devices.
Design, train, and deploy ML models and data pipelines using PyTorch/TensorFlow and MLOps tools in a full lifecycle workflow.
Job Purpose The Senior Cloud Security Engineer will design, implement, and maintain security controls across our multi-cloud environment, with a particular emphasis on securing AI/ML workloads and leveraging AI-driven…
Builds and deploys AI-powered applications by integrating LLMs, developing full-stack features (React/Next.js frontends, FastAPI/Flask backends), and optimizing RAG pipelines with vector databases for scalable, production-ready solutions.
Build and deploy production-ready AI-powered apps using LLMs, full-stack dev, and cloud infrastructure; optimize performance, reliability, and security.
Design and build enterprise-grade MLOps/LLMOps platforms on AWS SageMaker and EKS, automating model lifecycle from training to monitoring and ensuring scalable, secure AI deployments.
Designs and builds enterprise-grade GenAI solutions on AWS (Bedrock, AgentCore) with LLMs, RAG pipelines, and agentic workflows, ensuring scalability, performance, and cost efficiency.
Build and deploy AI/ML systems for drug discovery and patient care, including GenAI, RAG, and agents, using Python, cloud platforms, and MLOps/LLMOps pipelines.
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