> Markdown version of [/jobs/ext/240202-ml-infrastructure-architect](https://www.wearedevelopers.com/jobs/ext/240202-ml-infrastructure-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Infrastructure Architect - **Company:** OpenKyber LLC - **Location:** United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Python (Programming Language), Machine Learning, Tensorflow, TypeScript, Feature Engineering, Pytorch, Large Language Models, Model Validation, Scikit Learn, Kubernetes, HuggingFace, Machine Learning Operations - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7ddeded6c2426e9a ## About the Role JD At least 7 + years of experience, Strong in Gen AI, ML, Python, TypeScript. Palantir Foundry is an advantage Design and implement AI/ML models tailored to specific business problems (preferably Insurance), including generative LLM models and traditional ML approaches. ## Description Select appropriate algorithms and architectures based on data characteristics, performance requirements, and use-case complexity. Conduct feature engineering, hyper parameter tuning, and model validation to optimize performance and generalizability. Strong proficiency in Python, TypeScript and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Evaluate model performance using statistical metrics and real-world testing, ensuring robustness and fairness. Collaborate with cross-functional teams (business, product managers) to integrate models into production environments. Monitor, maintain, and retrain models to ensure continued accuracy, relevance, and compliance with ethical standards. Document model development processes for reproducibility and knowledge sharing across teams. Stay current with advancements in ML algorithms, generative architectures (e.g., transformers, graph neural networks), and tooling (e.g., MLflow, Kubeflow, Hugging Face) and so on. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Do TypeScript without TypeScript](https://www.wearedevelopers.com/videos/327-do-typescript-without-typescript) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Vuejs and TypeScript- Working Together like Peanut Butter and Jelly](https://www.wearedevelopers.com/videos/127-vuejs-and-typescript-working-together-like-peanut-butter-and-jelly) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)