We are looking for a Senior Machine Learning Engineer to work on modern AI and Large Language Model systems. This role combines strong machine learning fundamentals with hands-on engineering and focuses heavily on evaluating and improving AI systems. You will work with LLM-based solutions, including Retrieval-Augmented Generation (RAG), agentic systems, safety mechanisms, and end-to-end AI products. You will help define measurable objectives, develop evaluation methodologies and benchmarks, analyze model behavior, identify failure modes, and turn evaluation results into practical improvements. The role requires regular collaboration with US-based teams. Availability for overlap until 3 PM US Eastern Time is required. Essential functions Responsibilities 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 and identify failure modes Translate evaluation results into practical recommendations and improvements Build and maintain machine learning pipelines, tooling, and evaluation infrastructure Collaborate with product, engineering, and research teams Translate business and product goals into measurable machine learning objectives Rapidly prototype, test, and iterate on potential solutions Communicate findings, trade-offs, and recommendations to technical and non-technical stakeholders Qualifications 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 Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX Experience training, fine-tuning, or adapting machine learning models Practical experience with Large Language Models beyond simple API integration Experience evaluating AI/ML systems and translating evaluation results into actionable recommendations Experience building and maintaining machine learning systems and pipelines Understanding of Retrieval-Augmented Generation (RAG) Understanding of agentic AI systems Understanding of LLM safety concepts Ability to work effectively with ambiguous requirements and limited data Strong written and verbal communication skills Ability to overlap with the US-based team until 3 PM US Eastern Time Would be a plus Experience designing benchmarks, evaluation frameworks, or automated evaluation systems Experience with distributed model training Experience with large-scale model inference Experience building reusable machine learning tooling or internal ML platforms Experience with cloud platforms Experience with modern MLOps practices Experience working on user-facing AI products at scale Research experience in Machine Learning or Artificial Intelligence Publications in ML or AI-related fields We offer Opportunity to work on bleeding-edge projects Work with a highly motivated and dedicated team Competitive salary Flexible schedule Benefits package - medical insurance, sports Corporate social events Professional development opportunities Well-equipped office About us Grid Dynamics (

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GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in , supported by profound expertise and ongoing investment in , , , and . Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.