Tessero
Senior Machine Learning Engineer
Skills
18 days ago
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Important information
Contract type:
Permanent
Location:
Budapest, Hungary
Work mode:
Hybrid
Published on:
8 October 2026
What they need
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.
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