Develop and implement an AI-based Real-Time Mesoscale Analysis (AI-RTMA) system to improve weather forecast quality for NOAA. This includes designing data pipelines and conducting reviews of state-of-the-art AI data assimilation methodologies.
Remote Machine Learning Engineer Jobs (514)
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Develop, deploy, and maintain predictive and optimization machine learning models to solve complex business challenges. Design feature engineering pipelines and utilize MLflow for model lifecycle management and production monitoring.
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Lead the development of production-grade ML infrastructure and evaluation frameworks for AI-powered cybersecurity solutions. Drive the entire ML lifecycle from experimentation to deployment while optimizing AI agent performance and scaling infrastructure.
Design and maintain robust data pipelines for ingestion and feature engineering while developing and deploying ML models for classification, regression, and NLP. The role involves managing MLOps infrastructure and fine-tuning LLMs to translate data insights into actionable AI features.
Design, build, and maintain scalable machine learning models to automate logistics processes for wholesale distributors. Own the end-to-end ML pipeline from problem definition and data exploration to production deployment and monitoring.
The role involves fine-tuning transformer-based models and implementing real-time production pipelines using LLMs and multimodal models. Responsibilities include building evaluation frameworks, deploying models at scale, and collaborating with cross-functional teams to deliver intelligent solutions.
Build statistical models and production systems to optimize advertiser performance and business goals within the mobile ad tech marketplace. Design and execute experiments to validate economic theories and develop applications for budget retention and bidding innovation.
The role involves designing, prototyping, and deploying production-ready machine learning models and systems. Responsibilities include setting up monitoring protocols, optimizing MLOps workflows, and collaborating with platform and product teams.
Design, develop, and deploy machine learning and generative AI models to solve complex business challenges. Collaborate with MLOps and cross-functional teams to optimize and maintain scalable AI solutions in production.
Develop and deploy end-to-end agentic, full-stack AI systems for defense and intelligence customers. Build ML services using LLMs, embeddings, and RAG within production and air-gapped environments.
Build, train, and improve production-ready machine learning models while developing evaluation and feedback loops. Collaborate cross-functionally to implement ML-powered capabilities and establish tooling for monitoring model quality.
Develop and integrate ML, NLP, and Generative AI models to build AI-powered workplace assistants. Create agentic workflows and LLM-powered solutions while collaborating with engineering and product teams.
Design, deploy, and maintain production-grade machine learning models and pipelines using healthcare and pharmacy data. Implement MLOps best practices and ensure all systems comply with HIPAA and internal data governance regulations.
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The role involves preparing datasets, training and optimizing ML models, and maintaining inference services to improve search and retrieval quality. You will collaborate with cross-functional teams to build AI systems for ranking and recommendations, including LLM workflows.
The role involves preparing datasets, training and optimizing ML models, and maintaining inference services to improve search and retrieval quality. You will collaborate with cross-functional teams to build AI systems for ranking and recommendations using both classical ML and LLM workflows.
Machine Learning Engineer
Shuru Technologies Private Limited
·
Full Time
·
2 Weeks Ago
Shuru Technologies Private Limited
The MLOps Engineer will design, build, and maintain scalable machine learning pipelines and cloud-based environments. They will collaborate with cross-functional teams to deploy, monitor, and govern enterprise ML solutions to drive business impact.
The role involves designing, developing, and deploying scalable machine learning models to solve real-world problems. The engineer will collaborate with cross-functional teams to integrate these models into production systems and optimize them for performance.
Design and deploy agentic LLM solutions to detect and prevent fraud, abuse, and security threats at scale. Collaborate with cross-functional teams to integrate these systems into production and identify product vulnerabilities.
Design and deploy agentic LLM solutions to detect and prevent fraud, abuse, and security threats at scale. Build production-grade ML systems on cloud platforms and collaborate with cross-functional teams to safeguard the GitHub platform.
Develop and scale AI-powered experiences focusing on NLP and Large Language Models. Design evaluation frameworks and safety guardrails to ensure model reliability and performance in production.
Design and build scalable systems for real-time fraud detection, including data pipelines and backend services in Go. Develop and deploy ML models while ensuring high standards of security, privacy, and observability.
Design and build scalable ML data pipelines and translate R&D outputs into reusable libraries and products. Collaborate with customers and simulation engineers to embed AI models into practical engineering tools.
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Design and engineer AI-powered features to extract meaning from voice and messaging data at scale. Implement end-to-end ML pipelines and monitor production inference services to ensure operational health.
Design and engineer AI-powered features to extract meaning from voice and messaging data at scale. Implement end-to-end ML pipelines and monitor production inference services to ensure operational health.
Design and engineer AI-powered features to extract meaning from voice and messaging data at scale. Implement end-to-end ML pipelines and monitor production inference services to ensure operational health.
Design and develop AI-powered features to extract meaning from voice and messaging data at scale. Implement end-to-end ML pipelines and monitor production inference services to ensure operational health.
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