Expert AI Agentic Developer

Datamatics Technologies; Województwo mazowieckie; On-site; PLN 180,000 - 240,000; Full time

Job summary

Datamatics Technologies seeks a senior scientist to own end-to-end agentic AI initiatives, from concept to production, delivering measurable business outcomes. You will design agents, enable tool use, and collaborate with business leaders to scale solutions across the bank's private banking domain.

You will apply governance and safety practices, build reusable patterns, and monitor deployed agents to improve cost, latency, and reliability in a regulated environment.

Qualifications

• Hands-on agentic AI experience, including designing and building agent workflows.
• Strong LLM operations and evaluation mindset focusing on latency, cost and safety.
• Guardrails and Responsible AI practices for regulated environments.
• Strong Python software engineering with testing and version control.
• Experience deploying AI services on AWS and REST API integration.
• Familiar with containerisation, Kubernetes and CI/CD pipelines.
• Data/analytics platform experience (Snowflake) for grounding and context management.

Responsibilities

• Own delivery from idea to production with measurable outcomes.
• Build agentic AI applications and integrate with enterprise systems.
• Partner with stakeholders end-to-end to deliver scalable value.
• Engineer for governance, safety, and model risk in banking context.
• Enable reuse with patterns, components, and reference implementations.
• Monitor production agent behavior and iterate on prompts and tooling.

Skills

Agentic AI

LLM Ops

Python

Cloud AWS

Kubernetes

Data Analytics

Stakeholder mgmt

Tools

REST APIs

Containerisation

CI/CD

Snowflake

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.