Expert AI Agentic Developer

Datamatics-Global-Services-Ltd; Stockholms kommun; On-site; SEK 1,100,000 - 1,400,000; Full time

Job summary

Datamatics-Global-Services-Ltd is seeking a senior scientist to lead end-to-end delivery of agentic AI solutions. You will design and build agent workflows, ensure governance and safety, and collaborate with banking stakeholders to deliver scalable, high-value outcomes.

You will apply hands-on agentic AI, LLM operations, and robust software engineering (Python) in a cloud context (AWS) with containerization (Kubernetes) and Snowflake for grounding and retrieval.

Qualifications

• Hands-on agentic AI 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.
• Guardrails and Responsible AI experience: input/output validation and controls for regulated environments.
• Strong Python software engineering skills (modular design, testing, version control).
• Cloud and integration experience: deploying AI services in AWS and REST API integration.
• Familiarity with containerisation (Kubernetes) and CI/CD pipelines.
• Data/analytics platform experience (Snowflake) for grounding, retrieval, context management.
• Business engineering strength: translate stakeholder needs into requirements, KPIs, adoption plans.

Responsibilities

• Own delivery from idea to production with measurable outcomes.
• Design and implement agents that can reason, plan, use tools, and interact with enterprise systems.
• Collaborate with business leaders, architects, engineers, and control functions to ensure scalable value.
• Implement evaluation strategies, guardrails, and human-in-the-loop patterns for governance and safety.
• Build reusable patterns, components, and reference implementations to accelerate delivery.
• Monitor agent behavior in production and iterate on prompts, tools, and logic to improve quality, cost, latency, and safety.

Skills

Agentic AI

LLM Ops

Responsible AI

Python

AWS

REST APIs

Kubernetes

Snowflake

Multi-agent

Tool calling

Tools

Snowflake

Kubernetes

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.