18 beste Stellen als software entwicklerinnen / software entwickler im bereich webdevelopment, full stack in wien – aktuell auf Personalsuche

Senior Machine Learning Engineer
Smartstream Limited —Wien, W
- Vollzeit
- Master
Direkt bewerben
Go Developer (all genders)
Genetec —Wien, W
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8 Tage
Country Sales Leader, Splunk Austria
Cisco Systems —Wien, W
- Vollzeit
Senior Enterprise Account Executive
BLP Digital AG —Wien, W
- Vollzeit
- Homeoffice
10 Tage
Senior Specialist Corporate Communications & Fashion PR (m/w/d)
Peek & Cloppenburg B.V. & Co. KG —Wien, W
- Vollzeit
1 Tag
Revenue Operations Manager (all genders)
fiskaly —Wien, W
- Vollzeit
- Homeoffice
Direkt bewerben
Account Executive - MuleSoft / Data Foundation
Salesforce —Wien, W
- Vollzeit
Consultant (m/w/d)
d-fine GmbH —Wien, W
- Vollzeit
- Master
eCommerce Data Analyst (m/w/d)
woom Bikes —Wien, W
- Vollzeit
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AI Architect (m/w/d)
Capgemini —Wien, W
Data Platform Architect (m/f/d)
WOLF THEISS Rechtsanwälte GmbH & Co KG —Wien, W
- Vollzeit
Senior Databricks Platform Architect (all genders)
RHI Magnesita —Wien, W
- Vollzeit
1 Tag
AI Architect (m/w/d)
Capgemini Sogeti —Wien, W
Technical Product Generalist
GoStudent —Wien, W
- Vollzeit
- Master
Direkt bewerben
VP Commercial Operations & Transformation (m/f/d)
191 - Hellmann Worldwide Logistics International GmbH —Wien, W
- Master
Direkt bewerben
6 Tage
Senior Software Engineer (m/f/d) Full Stack, Product Engineering
HRworks GmbH —Wien, W
9 Std.
POS2617: IT Application Specialist
IMBA – Institut für Molekulare Biotechnologie GmbH —Wien, W
- Master
5 Std.
Head of Finance
fonio —Wien, W
- Vollzeit
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6 Std.

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Senior Machine Learning Engineer

Smartstream Limited -
Wien, W

Direkt bewerben

Details zur Stelle

Vollzeit

Qualifikationen

Master

Vollständige Stellenbeschreibung

We are looking for a Senior Machine Learning Engineer to build, ship, and operate the machine learning and AI solutions at the core of SmartStream's financial data processing and reconciliation platforms. Working with our data scientists, you will turn prototypes into cohesive, production-ready systems, using large and complex financial transaction datasets to power capabilities such as transaction matching, reconciliation, and exception handling. You will work across the full spectrum of applied AI, from classical machine learning (supervised, unsupervised, and deep learning) to agentic AI solutions built on large language models, tool use, and multi-step reasoning.

This is a hands-on engineering role. The emphasis is on productionising: turning models into robust, well-tested, observable services and keeping them accurate and reliable in production. You will own existing ML services end to end and evolve them, working closely with software engineers, data scientists, product managers, and domain experts to turn real-world reconciliation challenges into dependable software.

Job Responsibilities

Develop, deploy, and maintain machine learning models and services, and keep existing ones performant and robust
Translate research artefacts and prototypes into production-grade ML systems: hardening code, adding tests and observability, and owning deployment, scaling, and lifecycle management.
Own model serving, monitoring, drift detection, and retraining in production
Engineer and evaluate features on real financial datasets, and calibrate and validate models for reliable behaviour
Collaborate with software engineers and data scientists on the surrounding data and matching platform
Document methods and decisions to keep models transparent and reproducible

Requirements

Strong software engineering in Python: clean, typed, well-tested code, version control, and CI/CD
Strong proficiency with the scientific Python stack (NumPy, Pandas, scikit-learn, PyTorch) and a solid, practical grasp of machine learning, statistics, and model evaluation
Experience taking ML models into production and operating them there (serving, monitoring, retraining), not just building them in notebooks
Experience building and running production services and APIs (e.g. FastAPI or Flask), containerised and deployed on Kubernetes or similar
Feature engineering on structured/tabular data, and sound model evaluation and validation
Ability to work with large datasets and build reliable data pipelines
Clear communication with technical and business stakeholders

Desirable Skills

MLOps practices: model and data versioning, automated retraining, monitoring, and champion/challenger evaluation
Distributed data processing (e.g. Dask, Spark, Apache Arrow/parquet) and handling columnar data at scale
Deeper neural-network / PyTorch experience
Model explainability (e.g. SHAP) and probability calibration
Experience with LLM-based or agentic systems (tool use, orchestration, retrieval)
Familiarity with workflow orchestration and event/stream processing is a plus
Experience in regulated or data-intensive industries, ideally financial services
Familiarity with cloud-based ML infrastructure

Qualifications

Degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience

Experience

4-6+ years in machine learning engineering, or software engineering with a strong ML component
Experience delivering and operating ML models in production
Experience working in cross-functional teams delivering software products
Strong problem-solving skills and a pragmatic, ownership-driven approach to shipping reliable software

Equality Statement

Smartstream is an equal opportunities employer. We are committed to promoting equality of opportunity and following practices which are free from unfair and unlawful discrimination.

Direkt bewerben

Erweiterte Suche

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