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Senior AI/ML engineer role in Singapore (onsite) focused on building Python-based ML applications: predictive models and time-series forecasting, scalable data pipelines on Databricks using PySpark and SQL, and REST services via FastAPI, Flask or Django, in collaboration with data scientists and engineers.
The Machine Learning Engineer will provide MLOps support for autonomous trucking model development, including debugging pipelines, maintaining infrastructure, and collaborating with engineering teams. The role focuses on ensuring reliable model deployment using Python, PyTorch, and various MLOps orchestration tools.
Build and improve ML models for personalized recommendations and ad targeting at a Korean local-commerce platform, using deep learning and LLM techniques.
Builds and serves ML models to personalize search results for a local-commerce app, using NLP, graph-based ranking, and real-time inference.
Build and maintain ML infrastructure—LLM routers, metadata systems, and model-serving pipelines—so product teams can ship AI features faster and more reliably.
Machine Learning Engineer bridging the sim2real gap for Gravis Robotics' autonomous construction machines: builds ML models of machine dynamics, defines validation metrics for model fidelity and sim2real transfer, and monitors performance changes over time. Core stack is Python, PyTorch, and git, with RL and system-identification experience valued.
A 12-month maternity leave cover role building and productionising machine learning models on local-authority data: designing training pipelines, LLM/RAG and embedding-based models, and containerised services deployed into Xantara's OneView platform. Core stack: Python, ML frameworks, SQL, Docker/FastAPI, with Azure cloud a bonus.
Owns the product roadmap for a machine learning platform that supports the full ML lifecycle, from data and feature readiness through model deployment and observability, while collaborating with data scientists, ML engineers, and platform teams.
Builds and maintains machine learning infrastructure for AB InBev’s B2B platform BEES, including training pipelines, inference services, and monitoring, using Python, PySpark, Kubernetes, and Azure cloud.
Senior Machine Learning Engineer at AB InBev Growth Group in Campinas, Brazil, building and scaling ML pipelines for the BEES B2B platform using Python, PySpark, Kubernetes, and Azure Cloud.
Build and scale ML systems for BEES, AB InBev’s B2B commerce platform, owning end-to-end pipelines from data to production inference.
Build and maintain ML platform components for a B2B e-commerce SaaS, including training pipelines, inference services, and observability, using Python, PySpark, Kubernetes, and Azure.
Title: Solution Architect [AI/ML] Location- Boston, MA NOTE: PLEASE DO NOT SHARE PROFILES FROM DEVOPS BACKGROUND • Strong Heath care experience is needed. • Must have lead experience as Lead - Solution Architect • Also…
ML Engineer Location-Remote Job Summary We are looking for an ML Engineer to design, develop, deploy, and maintain machine learning models and solutions. The role involves working with data, developing ML algorithms,…
Primary Responsibilities: Lead the design and development of ML pipelines for advanced AI algorithms and ML models to solve complex problems across diverse business domains. Collaborate on development of ML…
Role: AI/ML Python Engineer Location: Iselin, NJ Duration: 12 Months Contract Position Overview: Bank is looking for a highly skilled AI/ML developer to design, develop, and deploy machine learning and generative AI…
Hi, Hope you are doing well, Please find the job description given below and let me know your interest. Position: Sr. Machine Learning Engineer (Hybrid) Location: Chicago, IL (Local only) Duration : 6-12 months Job…
Senior Software Engineer builds observability tools and agentic interfaces for AI workloads, using Go, Kubernetes, and telemetry systems like Prometheus and Grafana.
Develops and optimizes AI model-serving systems on GPU infrastructure, focusing on latency, reliability, and cost while working with tools like Triton, vLLM, and Kubernetes.
Staff Applied Research Engineer on CoreWeave's OpenPipe team, developing self-improving AI agents that learn from experience using reinforcement learning and LLM post-training techniques. Work spans from RL research to production systems, leveraging GPU-rich infrastructure and tools like Megatron and Kubernetes to solve bottlenecks in continuous agent learning.
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