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Senior Applied Machine Learning EngineerThis role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the…
Career Category Information Systems Job Description ABOUT AMGEN Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover,…
This role is eligible for our hybrid work model: Two days in-office. Based in our Mumbai office. Platform Engineer (MLOps & DataOps) The MLOps & Data DevOps team enables Priceline’s data engineering, machine…
Senior MLOps/AI Infrastructure Engineer for a fast-growing Bay Area AI company (hired via recruiter SmartRecruitment.com, iGaming-focused). Day to day: build and scale distributed training infrastructure, low-latency LLM/multimodal model serving, ML CI/CD, and Kubernetes/cloud compute optimization. Remote role open to candidates in Brazil, Malta, or Taiwan.
Founding platform/DevOps engineer at an enterprise AI company in Hyderabad, owning AI infrastructure end to end: GPU-based LLM model serving, MLOps pipelines, Kubernetes, IaC, observability, and cloud/FinOps across AWS/GCP/Azure, with a growth path to platform engineering leadership.
Lead AI/ML Engineer at Oxiliry, a business process outsourcing company in Karachi: design, build, and deploy production ML/DL models (NLP, computer vision, predictive analytics), build data pipelines and MLOps practices, architect scalable AI systems, and mentor junior engineers. Core stack: Python with TensorFlow/PyTorch/Scikit-learn plus cloud deployment.
Generative AI engineer at consulting firm Thoucentric (Bengaluru): architect, fine-tune, and deploy LLM solutions for clients — building RAG pipelines, LangChain/LlamaIndex apps, MLOps, and GPU-optimized inference. Requires 5+ years in ML/DL with 2+ years in GenAI, strong Python (PyTorch/TensorFlow), and cloud AI platforms.
Senior AI Solutions Architect at Coaction Specialty (specialty insurance) — the company's first dedicated AI architect, defining the enterprise AI reference architecture (LLM integration, RAG pipelines, agentic AI platforms), security/governance standards, and AI observability, while mentoring engineers. Hands-on role requiring Python/C#/Java, AWS/Azure, and MLOps tooling.
Designs, builds, and operates Scotiabank's enterprise AI/ML platform: reusable services for model training, deployment, inference, monitoring, and governance, plus developer portals and self-service tooling. Core stack spans Python/Java, Azure/AWS/GCP, Docker/Kubernetes, MLOps/LLMOps, and React-style front ends.
Machine Learning Engineer at Wave (financial software for small businesses) who builds, trains, and deploys ML models and scalable pipelines/platforms into production. Core stack is AWS (SageMaker, Spark/Glue), Terraform, Airflow orchestration, and MLOps tooling like MLflow/Kubeflow, with a focus on governance, observability, and cost-efficient ML systems.
Senior AI Engineer at Velsera (PierianDx) in Pune, India, training, fine-tuning, and deploying LLMs and building RAG pipelines and AI/ML infrastructure on cloud platforms. Core stack: Python, PyTorch/TensorFlow/Hugging Face, vector databases, Docker/Kubernetes, and MLOps tools like MLflow.
Senior AI Engineer at ottonova, Germany's first digital private health insurer: builds and deploys generative AI use cases (LLMs, RAG, agentic workflows) end to end in Munich, and progressively owns the architecture and evolution of the company's AI platform. Requires 5+ years of AI/ML experience, cloud/MLOps skills (AWS a plus), and fluent German (B2+).
Senior AI engineer at RIVR (part of Amazon), a robotics company building autonomous last-mile delivery robots. Day to day you build the core data engine: pipelines that process, curate, filter, and pseudo-label terabytes of multi-modal sensor data (video, LiDAR, IMU, odometry), and develop self-supervised/representation learning models in Python/PyTorch to power downstream VLA and RL teams.
A senior engineer on WPP's Open Intelligence team in Copenhagen who designs and operates scalable, production-grade data and ML infrastructure, mainly on GCP, turning data-science prototypes into reliable platforms. Core stack includes Python, SQL, cloud platforms, PyTorch/TensorFlow, and MLOps tooling like MLflow or Kubeflow.
Senior Cloud Platform Engineer at Viamatica in Guayaquil, Ecuador: architect, deploy, and operationally support Microsoft Azure infrastructure (AKS, Azure Functions, networking) using IaC, CI/CD, and containers, with a focus on healthcare compliance (HIPAA/PHI), production reliability, cloud cost management, and AI-ready compute.
First MLOps hire at Clariti/CivCheck, building and scaling the ML infrastructure behind an AI plan-review platform for governments: end-to-end training, evaluation, and serving pipelines on cloud GPU infra, plus CI/CD, monitoring, and cost optimization for production ML. Core stack: Python, Docker, Kubernetes, AWS/GCP/Azure, MLflow/W&B/Kubeflow.
AI Solutions Engineer (Principal level) on Deltek's internal AI Center of Excellence, designing, building, and deploying production AI/ML applications for business problems. Day to day: generative AI with LLMs (RAG, prompt engineering, agents), Python APIs and microservices, MLOps pipelines, and cloud platforms (Azure/AWS/GCP) with AI governance and security.
Staff ML engineer building scalable offline and online machine learning platforms for Coupang's Search & Discovery team, working on ranking, semantic retrieval, query understanding, and personalization over billions of user-interaction records. Core tech: Python/Java, Spark, Airflow, Kubeflow, MLflow, transformer models, LLMs, and vector search.
Providence is seeking an exceptional Principal Cybersecurity Architect to lead the research, development, and deployment of Small Language Models (SLMs) specialized for healthcare and cybersecurity applications. This…
Description We are seeking a highly experienced Principal MLOps Engineer with 5–10 years of industry experience to lead the design, deployment, and optimization of machine learning infrastructure. This role requires…
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