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From 51 similar postings that came down. Coming down is not always a hire.
Data Scientist - MID Profile - BN 12/11/26
Reads like an AI Engineer role: building products on existing AI models.
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Employment terms need review — employment source
Employment type Permanent contract & B2B
Read the full posting
What you’ll work on
Full postingDesign and develop LLM-based agentic solutions to automate complex business processes
Develop data extraction pipelines and evaluation and observability frameworks for AI agents
Deploy and industrialize LLMs on GPUs with testing and DevOps best practices
Work with business teams to identify needs and drive adoption of AI solutions
From the employer’s posting
Design and develop LLM-based agentic solutions to automate complex business processes.
Develop data extraction pipelines and implement evaluation and observability frameworks for AI agents.
Deploy and industrialize models (LLMs, GPUs), ensuring quality through testing and DevOps best practices
Work closely with business teams to identify needs and drive the adoption of AI solutions.
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 11, 20262 days ago
- Recorded sightings
- 3
- Last seen by us
- Nov 13, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Margo-Group is hiring
6 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring is picking up
- Its jobs tend to stay up a long time
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Bash
- Python
- Kubernetes
- FastAPI
Where the posting names them
- Hands-on practice in pytest (unit, functional, and integration testing for ML/LLM pipelines). - Bash scripts for automation, data handling, and container interaction. - LLMs Architecture understanding proven by project portfolio.
Expectations: - Strong Python with extensive experience in Object-Oriented Programming and enterprise software design patterns. - Proven track record building asynchronous microservices and endpoints using FastAPI.
- Dedication to shipping stable, secure code into flagship banking infrastructure where compliance, auditability, and data security are non-negotiable. - General Stack: Google ADK, LangGraph, DSPy, vLLM, FastAPI, Python, Pytest. Arize Phoenix. Kubernetes, Jenkins, GPUs. Responsibilities:
- Strong Python with extensive experience in Object-Oriented Programming and enterprise software design patterns. - Proven track record building asynchronous microservices and endpoints using FastAPI. - Hands-on practice in pytest (unit, functional, and integration testing for ML/LLM pipelines).
Your next step
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Demand for AI Engineer roles
New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
Job description
Context:
A team responsible for the development and industrialization of applications for the Investment Banking division. Accelerating the integration of generative AI solutions and AI agents into business processes. A strategic environment within the platform, with high-impact use cases in document analysis, credit, and finance.
Expectations:
- Strong Python with extensive experience in Object-Oriented Programming and enterprise software design patterns.
- Proven track record building asynchronous microservices and endpoints using FastAPI.
- Hands-on practice in pytest (unit, functional, and integration testing for ML/LLM pipelines).
- Bash scripts for automation, data handling, and container interaction.
- LLMs Architecture understanding proven by project portfolio.
- Architecting multi-step, stateful agentic graphs using LangGraph or Google ADK.
- Hands-on experience optimizing LLM pipelines programmatically using DSPy.
- Practical knowledge of deploying and running high-throughput open-weight models on GPU infrastructure using runtimes such as vLLM (experience with CUDA and fine-tuning is a strong plus).
- Hands-on familiarity with automated build, test, and release pipelines (Jenkins).
- Experience setting up tracing, drift detection, and automated evaluations for generative workflows using tools like Phoenix Arize.
- Dedication to shipping stable, secure code into flagship banking infrastructure where compliance, auditability, and data security are non-negotiable.
- General Stack: Google ADK, LangGraph, DSPy, vLLM, FastAPI, Python, Pytest. Arize Phoenix. Kubernetes, Jenkins, GPUs.
Responsibilities:
- Design and develop LLM-based agentic solutions to automate complex business processes.
- Work closely with business teams to identify needs and drive the adoption of AI solutions.
- Develop data extraction pipelines and implement evaluation and observability frameworks for AI agents.
- Deploy and industrialize models (LLMs, GPUs), ensuring quality through testing and DevOps best practices
- You are taking full ownership in driving AI features.
- You remember to respect the boundaries and non-deterministic nature of LLMs, communicate constraints clearly, and build robust safety rails.
- You are a team player, communicate technical trade-offs cleanly with architects and business users, and enjoy mentoring peers.
- You are testing ideas and PoCs quickly, validate feasibility with data and avoid technical debt.
Career Prospects:
- Build expertise in Agentic AI architectures and the industrialization of LLMs in production. Work on high-visibility projects within the Investment Banking division.
Employment type
Permanent contract & B2B
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Warsaw
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.