Descripción del trabajo
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We are looking for a talented Senior Machine Learning Engineer - LLM Systems & Evaluation.

This is an opportunity to work on next-generation AI systems, including large language models, retrieval-augmented generation, agents, and AI safety-focused evaluation .

Essential functions:

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- 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

- Analyse model behaviour, 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

Qualifications:

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- 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

- Understanding of retrieval-augmented generation (RAG), agentic systems, and LLM safety concepts

- 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

- Ability to work effectively in ambiguous problem xqbhyrx spaces with incomplete requirements and limited data

- Strong written and verbal communication skills

Would be a plus:

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- 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

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📌 Senior Machine Learning Engineer (Mos)
🏢 Grid Dynamics
📍 Mos