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Develop and deploy machine learning and generative AI applications—including LLMs, RAG pipelines, and GenAI services—using Python, PyTorch, Azure, and vector databases in an on-site role at HP.
MLOps + DevOps Engineer responsible for operating an AI-native platform with agentic systems, ML workloads, and backend services on AWS. Core technologies include AWS (EKS, Lambda, Bedrock, SageMaker), Kubernetes, Kafka, Terraform, and observability stacks like Grafana, Prometheus, and OpenTelemetry.
Build and deploy AI-powered applications for clients by integrating Python, LLMs, and Generative AI into scalable systems while collaborating directly with stakeholders.
Architect and build a globally distributed Model-as-a-Service platform using Go, Kubernetes, PostgreSQL, Redis, and Kafka—handling API compatibility, token metering, multi-tenant security, and production reliability for a crypto infrastructure company.
This role involves developing and implementing Generative AI solutions within Google Ads, focusing on ML infrastructure, model optimization, and system development. Engineers will collaborate on full-stack projects, debug complex system issues, and contribute to the evolution of advertising products using large-scale AI technologies.
Build predictive models, time-series forecasts, and generative-AI tools to automate sales insights and optimize revenue for AMD’s Sales Operations team using Python, SQL, and ML frameworks.
Singtel is where Technology meets Purpose. We're building an AI-first telco for the future—connecting people, businesses and communities through trusted networks, intelligent digital services and next-generation…
Data Scientist developing, deploying, and monitoring machine learning models on-premises and in the cloud (AWS/Databricks) using Python and SQL at a Philippine telecom company.
An AIOps/MLOps Engineer designing and maintaining MLOps pipelines, AI/ML infrastructure on Azure, CI/CD automation, and monitoring/observability solutions while collaborating with data scientists and software engineers.
Senior Product Manager leading end-to-end AI platform planning for AMD's Client products, defining requirements across silicon, software (ROCm), and AI ecosystems while driving performance strategy and go-to-market execution.
Design and deliver enterprise-grade generative AI solutions using Azure OpenAI, LangChain, and RAG pipelines to build AI agents and scalable AI infrastructure.
Build and operate Upstart's ML and simulation platform infrastructure—model training, feature engineering, inference, and marketplace simulation—using Python, Kotlin, AWS, Spark/Databricks, and MLOps tooling.
Develops AI/ML-powered software systems for Google’s gUP Customer Support, focusing on user voice and AI assistant features. Works on full-stack solutions, model integration, and 24x7 support systems while collaborating with engineering, product, and UX teams.
Leads platform, SRE, and MLOps teams to build and scale cloud-native infrastructure for an AI-driven climate-tech startup, enabling reliable, secure systems for wildfire risk prediction and grid resilience.
Build and scale Generative AI features for Google Workspace products like Gmail and Docs. The role involves full-stack software development, design reviews, and debugging, heavily leveraging ML infrastructure, LLMs, and multi-modal vision models to impact billions of users.
A Data Scientist II designs and deploys AI models for customer engagement, partnering with clients to integrate data pipelines and reinforcement learning systems using Python, SQL, and ML libraries.
Applied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable…
Develops GenAI-powered content understanding and generation frameworks for Google Search, integrating cutting-edge AI models into scalable systems to enhance content retrieval and user experiences across platforms.
Senior Software Engineer in Google's Labs incubation group, building and deploying ML/AI solutions—including generative AI, ML infrastructure, speech/audio, or reinforcement learning—at scale across Google's products.
Build and maintain scalable MLOps infrastructure on AWS to automate ML model training, deployment, and monitoring for a B2B sales-intelligence platform.
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