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 Machine Learning Engineer to join their expanding team.
- Build and maintain end-to-end ML pipelines, including feature engineering, model training, evaluation, deployment, and monitoring using Databricks and AWS.
- Develop and operate Agentic AI solutions using frameworks such as LangChain and LangGraph, contributing to workflow automation and orchestration improvements.
- Design and maintain feature engineering pipelines, ensuring data quality, consistency, and availability across batch and real-time environments.
- Support and enhance MLOps practices, including CI/CD, experiment tracking, model versioning, automated retraining, and monitoring with MLflow and Databricks.
- Develop LLM-powered applications, including RAG pipelines, vector database integrations, prompt management, and tool-use capabilities.
- Monitor model performance by tracking drift, latency, errors, and costs, and proactively addressing production issues.
- Write high-quality production code with strong testing, documentation, and engineering standards.
- Provide operational support through incident response, troubleshooting, and participation in on-call rotations.
- Collaborate across teams, partnering with Data Engineers and Data Scientists to move ML solutions from research into production.
What we're looking for
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3+ years of experience in Machine Learning Engineering, Applied ML, or Software Engineering with recent production ML delivery.
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Production ML deployment experience, including operating, monitoring, and maintaining models serving real-world inference traffic.
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Strong knowledge of the Databricks ML ecosystem, including MLflow, Model Serving, and Feature Store.
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Experience with AWS services supporting ML workloads (e.g., SageMaker, S3, Lambda, Step Functions, ECS/EKS).
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Hands-on experience building Generative AI / LLM applications, including:
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Retrieval-Augmented Generation (RAG)
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Vector databases
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Prompt engineering
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Integration with OpenAI, Anthropic, Bedrock, or similar APIs
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Experience with Agentic AI frameworks and multi-step workflow orchestration using LangChain, LangGraph, or similar tools.
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Strong Python programming and software engineering fundamentals, including testing, CI/CD, version control, code reviews, and debugging.
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Experience with leading ML frameworks such as PyTorch, TensorFlow, scikit-learn, or XGBoost in production environments.
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Familiarity with containerization and deployment technologies, including Docker, Kubernetes, ECS, and infrastructure-as-code concepts.
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Professional working proficiency in English with strong communication skills
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