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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Grid Dynamics (nasdaq: Gdyn) - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Distributed Computing Environment, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Pytorch, Large Language Models, Multi-Agent Systems, Model Validation, Generative AI, Information Technology, Machine Learning Operations - **Published:** August 21, 2026 - **Apply:** https://www.dice.com/job-detail/d5c923cd-0a34-4903-a99c-a8363b39671b ## About the Role We are looking for a Machine Learning Engineer with practical engineering skills and a deep understanding of modern AI systems, including LLMs, RAG architectures, agents, and safety considerations.Success in this role requires the ability to work in ambiguous environments, define measurable objectives, create evaluation methodologies when none exist, and rapidly iterate toward effective solutions., * 5+ years of experience in Machine Learning Engineering or a related field. * Strong understanding of machine learning fundamentals and model evaluation. * Strong Python programming skills and experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX. * Experience training, fine-tuning, or adapting machine learning models. * Experience working with Large Language Models beyond simple API integration. * Experience evaluating AI systems and translating results into actionable recommendations. * Experience building and maintaining machine learning systems and pipelines. * Understanding of retrieval-augmented generation (RAG), agentic systems, and LLM safety concepts. * Ability to work effectively in ambiguous problem spaces with incomplete requirements and limited data. * Strong written and verbal communication skills. * Bachelor's degree in Computer Science or equivalent is required, * Experience designing benchmarks, evaluation frameworks, or automated evaluation systems. * Experience with distributed training or large-scale model inference. * Experience building reusable ML tooling and internal platforms. * Experience with cloud platforms and modern MLOps practices. * Experience working on user-facing AI products at scale. * Research experience or publications in machine learning or AI-related fields ## Description * Own machine learning projects from problem definition through implementation. * Design and implement evaluation methodologies for AI and machine learning systems. * Create datasets, benchmarks, and metrics to measure model and product performance. * Evaluate and improve LLM-based systems, including RAG applications, agents, safety systems, and end-to-end AI products. * Analyze model behavior, identify failure modes, and recommend practical improvements. * Build and maintain ML pipelines, tooling, and evaluation infrastructure. * Collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives. * Prototype and iterate rapidly to solve business and product challenges. * Communicate findings, trade-offs, and recommendations to both technical and non-technical stakeholders. ## Related Videos - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Your imaginations is (no longer) the limit: how Generative AI empowers people to be creative](https://www.wearedevelopers.com/videos/741-your-imaginations-is-no-longer-the-limit-how-generative-ai-empowers-people-to-be-creative) - [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) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## 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) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [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 – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? 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