Senior Machine Learning Engineer

**Senior Machine Learning Engineer** Our partner is a dynamically growing company with a strong international background, leveraging data-driven operations to support advanced analytics and AI/ML-based business processes. Working in a modern technology environment, the company places a strong emphasis on the development and operation of scalable data platforms that drive innovation and business value. We are looking for a **Senior** **Machine Learning Engineer** to join their expanding team. **What we're looking for** * **6+ years** in Machine Learning Engineering or Applied ML, with proven production delivery. * Experience building and operating **production ML systems at scale** . * Strong expertise in **Databricks ML** , AWS, and MLOps practices. * Hands-on experience with **LLMs, RAG pipelines, vector databases, and Agentic AI frameworks** (LangChain, LangGraph, AutoGen). * Strong **Python** skills and experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or XGBoost. * Knowledge of **real-time and batch model serving** , performance optimization, and cost management. * Strong software engineering fundamentals including testing, CI/CD, version control, and code reviews. **Your responsibilities** * Design, build, and operate **end-to-end ML pipelines** for training, deployment, serving, and monitoring on AWS and Databricks. * Develop **Agentic AI solutions** using frameworks such as LangChain, LangGraph, and CrewAI with strong reliability and governance controls. * Build and optimize **feature stores, feature engineering pipelines, and training datasets** supporting batch and real-time inference. * Own and enhance **MLOps processes** , including CI/CD, automated retraining, model versioning, experiment tracking, and A/B testing. * Design and implement **LLM applications** including RAG architectures, prompt management, tool integration, and memory management. * Establish **model monitoring and evaluation frameworks** covering performance, drift, accuracy, latency, costs, and system reliability. * Collaborate with Data Engineering teams to ensure high-quality, reliable data for ML systems. * Provide technical leadership through mentoring, code reviews, architecture guidance, and technology evaluation. **What our partner offers** Hybrid working model Flexible working environment International and innovative technology landscape Opportunity to work on exciting and complex projects **Ready for your next challenge?** If you're passionate about building and scaling production machine learning systems, deploying AI solutions that deliver real-world impact, and working at the intersection of predictive models, Generative AI, and modern MLOps practices, we'd love to hear from you.