Job
- Level
- Senior
- Job Field
- IT, Data, DevOps
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Salary
- from 67.400 € Gross/Year
- Location
- Vienna
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you will take end-to-end ownership of ML/AI applications, optimize RAG, and establish LLMOps while collaborating closely with business partners and implementing evaluation frameworks.
Job Technologies
Your role in the team
- End-to-end ownership of ML/AI/agentic use cases: framing to production.
- Implement MCP and orchestrate LLMs/tools with agent frameworks (LangChain, LlamaIndex, Semantic Kernel, OpenAI Assistants), including function calling, fallbacks, and guardrails.
- Build and optimize RAG; evaluate for precision/recall and latency.
- Establish LLMOps/MLOps (experiments, versioning, CI/CD, registries, monitoring, incident response) and ensure reliability, safety, and compliance (prompt-injection defenses, content filtering, policy, red teaming, quality gates).
- Run rigorous offline/online evaluations (backtests, time-series CV, A/B, shadow/canary), monitor drift/impact, and optimize performance/cost (latency, throughput, rate limits, batching, streaming, caching) with usage/cost dashboards.
- Interact with Business Partner to Define requirements.
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Our expectations of you
Qualifications
- Expert Python skills; SQL/PySpark; Spark/Databricks and expertise in DS/ML/LLM.
- Hands-on with agent frameworks and tooling (LangChain, LlamaIndex, CrewAI or similar), prompt engineering, function/tool calling, and evaluation harnesses.
- RAG expertise: embeddings, vector stores, retrieval, and evaluation approaches.
- Ability to translate business requirements into production-grade technical solutions and to manage stakeholder relationships; fluent English required, German is an advantage.
Experience
- 5+ years delivering ML/AI systems in production; experience in financial services is an asset.
- Practical experience with GenAI models and pipelines and agentic AI (architectures, planning, memory, tool use, multi-agent orchestration).
- LLMOps / MLOps experience: MLflow or similar, feature stores, data/prompt versioning, CI/CD (GitHub Actions, Jenkins), Docker, orchestration tools (Airflow, Prefect).
- Experience designing evaluation and validation frameworks: backtesting, out-of-sample testing, A/B, drift detection.
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Benefits
Work-Life-Integration
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Topics that you deal with on the job
Job Locations
This is your employer
Raiffeisen Bank International AG
Wien, Wien
The Raiffeisen Bank International AG (RBI) is a leading commercial and investment bank in Austria and also considers Central and Eastern Europe (CEE) to be its home market. In CEE, the RBI has a tightly knit network of subsidiaries, leasing companies, and a variety of specialized financial service providers in 17 markets.
Description
- Company Size
- 250+ Employees
- Founding year
- 2010
- Language
- English
- Company Type
- Established Company
- Working Model
- Full Remote, Hybrid, Onsite
- Industry
- Banking, Finance, Insurance
Dev Reviews
by devworkplaces.com
Total
(4 Reviews)4.1
Engineering
3.6Career Growth
4.0Culture
4.4Workingconditions
4.7
Senior Data Scientist/ML Engineer/AI Engineer
- Salary
- from 67.400 € Gross/Year
- Location
- Vienna
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
