Senior Machine Learning Engineer - LLM Systems & Evaluation

ppWe are seeking a talented and experienced Senior Machine Learning Engineer to join our team, focusing on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, agents, and safety considerations. The ideal candidate has a strong foundation in machine learning, practical engineering skills, and a passion for advancing AI systems in ambiguous, fast-paced environments. /ph3About Our Client /h3pOur client is a global leader in technological innovation, committed to operational excellence and impactful solutions worldwide. /ph3Responsibilities /h3ulliLead end-to-end machine learning projects from problem definition to deployment /liliDesign and implement evaluation methodologies for AI and ML systems /liliDevelop datasets, benchmarks, and metrics to measure performance /liliEvaluate and optimize LLM-based systems, including RAG, agents, and safety modules /liliAnalyze model behaviors, identify failure modes, and recommend practical improvements /liliBuild and maintain ML pipelines, tooling, and evaluation infrastructure /liliCollaborate closely with product, engineering, and research teams to align ML objectives with business goals /liliPrototype rapidly and iterate to solve complex business and product challenges /liliCommunicate technical findings, trade-offs, and recommendations to diverse stakeholders /li /ulh3Requirements /h3h3Required: /h3ulli5+ years of experience in Machine Learning Engineering or related fields /liliDeep understanding of machine learning fundamentals and model evaluation techniques /liliStrong Python skills with experience in modern ML frameworks such as PyTorch, TensorFlow, or JAX /liliProven experience training, fine-tuning, or adapting large-scale models /liliHands-on experience working with LLMs beyond simple API integration /liliAbility to evaluate AI systems and translate results into actionable insights /liliExperience building and maintaining ML pipelines and systems /liliKnowledge of RAG architectures, agentic systems, and AI safety concepts /liliCapable of working effectively in ambiguous problem spaces with limited data and requirements /liliExcellent communication skills, both written and verbal /liliWillingness to work up to 9 pm Swiss time /li /ulh3Nice to have /h3ulliKaggle competition winners or notable programming contest achievements /liliML modeling experience /liliExperience in designing benchmarks, evaluation frameworks, or automated evaluation systems /liliExperience with distributed training and large-scale inference /liliBuilding reusable ML tooling and internal platforms /liliCloud platform expertise and modern MLOps practices /liliExperience working on user-facing AI products at scale /liliResearch publications or experience in ML/AI research /li /ulh3We offer /h3ulliOpportunity to work on bleeding-edge projects /liliWork with a highly motivated and dedicated team /liliCompetitive salary /liliFlexible scheduleBenefits package - medical insurance, sports /liliCorporate social events /liliProfessional development opportunities /liliWell-equipped office /li /ulh3About Us /h3pGrid Dynamics (NASDAQ: 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 enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India. /p /p #J-18808-Ljbffr