Machine Learning Engineer

at  Epam Systems

REQUIREMENTS

• 8+ years of strong background in machine learning, data science and software engineering
• Proficient with Python language and worked on various ML toolkits like TensorFlow, PyTorch, Keras, Scikit Learn
• Theoretical understanding of statistical models such as regression, clustering and ML algorithms such as decision trees, random forests, neural networks, etc
• Strong understanding of cloud computing and cloud AI services
• Experience in deploying AI/ML models in production environments
• Experience in data analytics, feature creation, model selection and ensemble methods, performance metrics and visualization
• Experience working with large datasets and distributed computing systems
• Experience in fine-tuning DL models including LLMs, SLMs
• Knowledge of large language models from OpenAI such as GPT 3.5, GPT 4, Codex etc
• Experience with Vector Stores and RAG pipelines
• Proficient with Data-Modelling Tools
• Proficient with multiple ML deployment strategies including static and dynamic
• Excellent knowledge of CI/CD pipelines for ML algorithms, training, prediction pipelines
• Experience in translating ML-based outcomes to business-digestible insights
• Excellent communication skills and experience in managing stakeholders at various levels
• Fluent English skill is a must, German language skill is a significant advantage

ABOUT THE ROLE

We are seeking an AI-ML Tech Engineer in Zurich who has a minimum of 8+ years of strong background in machine learning, data science and software engineering. The role requires a mix of remote and on-site work in a hybrid model.
As a Machine Learning Engineer you will develop and deploy machine learning models, work with large datasets and collaborate with cross-functional teams to solve business problems.

RESPONSIBILITIES

• Build ML (Machine Learning) & NLP (Natural Language Processing) solutions using common ML libraries and frameworks
• Choose, improve and apply various methods from statistics, machine and deep learning technologies to solve business problems
• Model the data of our users, their interests and online behavior to serve the business demands and enable the future business models
• Work closely with our data engineering team and other cross-functional teams to develop prototypes for cross-portfolio use cases and support the deployment of your models or algorithms into the cloud-native production environment
• Communicate and visualize the value of data by describing the findings as well as the way how techniques work to both technical and non-technical audiences
• Keep closely following research and engineering developments, like TensorFlow, PyTorch, Scikit and Keras
• Roll in the relevant technologies and methodologies to keep our modern technology landscape up to date