Expert AI Agentic Developer

Datamatics-Global-Services-Ltd; Madrid; On-site; EUR 90,000 - 130,000; Full time

Job summary

Datamatics-Global-Services-Ltd in Madrid seeks a senior scientist to own end-to-end delivery of AI solutions from problem framing to production. You will design agentic AI workflows, prototype quickly, and industrialize services with measurable outcomes, working across private banking contexts.

You will collaborate with business leaders, architects, and engineers, design governance and safety patterns, drive reusable patterns, monitor production, and improve cost, latency, and quality while

Qualifications

• Strong background in building AI-driven solutions from idea to production.
• Experience with agent-based architectures (planner-executor, tool calling, multi-agent).
• Proficiency in Python and software engineering best practices.

Responsibilities

• Own delivery from idea to production: frame business problems, shape AI designs, prototype quickly, and industrialize into production-ready services.
• Build agentic AI applications: design agents that can reason, plan, use tools, and interact with enterprise systems.
• Partner with Private Banking/AWM stakeholders to ensure scalable solutions that deliver real value.
• Engineer governance and safety: implement evaluation strategies, guardrails, and human-in-the-loop patterns.
• Enable reuse and scale: create reusable patterns and reference implementations across use cases.
• Operate and improve: monitor production, iterate on prompts/tools/logic, and optimize quality, cost, latency, and safety.

Skills

Python

LLM ops

Multi-agent systems

Kubernetes

REST APIs

Snowflake

Education

Master's degree in CS or related field

Tools

Snowflake

Docker

CI/CD

Job description

Main responsibilities in this role include, but are not limited to:

Own delivery from idea to production: frame business problems, shape agentic Al solution designs, prototype quickly, and industrialize into production-ready services with measurable outcomes.
Build agentic Al applications: design and implement agents that can reason, plan, use tools, and interact with enterprise systems (e.g., advisor tooling, analytics platforms, internal APls).
Partner with stakeholders end-to-end: collaborate with Private Banking/AWM business leaders, architects, engineers, and control functions to ensure solutions deliver real value and can be adopted at scale
Engineer for governance and safety: implement evaluation strategies, guardrails, and human-in-the-loop patterns suitable for regulated banking environments (privacy, auditability, model risk expectations).
Enable reuse and scale: build reusable patterns, components, and reference implementations to accelerate delivery across a portfolio of use cases.
Operate and improve: monitor agent behavior in production, iterate on prompts/tools/logic based on evals and feedback, and continuously improve quality, cost, latency, and safety.
Who you are

Collaboration. Ownership. Passion. Courage. These are the values that guide us in being at our best - and that we imagine you share with us. This is the right role for you if you are a curious, outcome-driven and pragmatic senior scientist who can take ownership, influence stakeholders, and raise modelling and engineering standards while delivering working solutions at pace.

Your background and skills include:

Are a curious, innovative team player with a can-do attitude, always looking for ways on how we can unlock the next level for our customers.
Hands-on agenticAl experience, including designing and building agent workflows (planner-executor, tool calling, multi-agent, human-in-the-loop).
Strong LLM ops and evaluation mindset: quality/correctness, latency, cost, and safety, ability to define test sets and run disciplined experimentation.
Guardrails and Responsible Al experience: input/output validation, prompt safety, data leakage prevention, and controls appropriate for regulated environments.
Strong software engineering skills with Python (modular design, testing, version control) and comfort working in notebook-based experimentation.
Cloud and integration experience: deploying Al services in AWS, integrating via REST APIs, and familiarity with containerisation/Kubernetes and CI/CD
Data/analytics platform experience (eg., Snowflake) for grounding, retrieval, and context management. Business engineering strength: ability to translate stakeholder needs into solution requirements, KPIs, and adoption plans; st communication and collaboration skills.