A major enterprise in the financial sector is looking for a Machine Learning Engineer to join its cross-functional AI & Data team. This role focuses on enabling production-ready Machine Learning pipelines and ensuring seamless integration of ML solutions into enterprise-grade systems.
You'll bridge the gap between Data Science and IT Operations by ensuring that deployed ML models are scalable, reliable, and continuously monitored, contributing to both the technical and business performance of AI-driven services.
Key responsibilities
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Collaborate with Data Scientists to define and build ML solutions with production constraints in mind.
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Design and implement automation for ML pipelines, including containerization (Docker/VM), testing (unit/integration/regression), and deployment using CI/CD tools.
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Support the integration of AI services using existing industrial tools and platforms.
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Work with IT Production teams to parameterize and maintain ML environments.
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Ensure model reliability, retraining strategies, and monitoring across technical and business dimensions.
Required skills & experience
Professional experience
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Minimum of 4 years of experience in a similar role
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Prior experience working within Agile methodologies
Technical expertise (mandatory)
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Containerization & Virtualization (Docker, VM)
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CI/CD tooling (GitLab CI or equivalent)
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AI platforms and development environments (IDEs)
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Version Control (code, model, data)
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Python programming
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Dependency/package management
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PostgreSQL
Technical expertise (nice to have)
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Integration across varied tech stacks (e.g., distributed systems, mainframes)
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Model optimization (e.g., compression techniques)
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ELT / ETL pipelines
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Big Data processing tools (e.g., Spark)
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Data visualization and real-time data flow tools
Soft skills
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Strong communication (written and oral)
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Results-oriented with attention to detail
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Creative problem-solver and continuous learner
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Efficient, proactive, and adaptable
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Team player who thrives in a dynamic, evolving environment
