Why This Role Matters
SimCorp's consulting delivery involves solving complex, recurring problems across dozens of clients. The purpose of this team is to turn those recurring challenges into scalable, AI-enabled capabilities — reducing manual effort, increasing consistency, and raising quality across engagements.
As an AI Engineer, you will contribute to that mission from the start — building real AI tools used in live projects, with the support and structure to develop into a confident, independent engineer over time. As the team grows, you will have the opportunity to influence how these tools are designed, built, and used across consulting delivery — not just contribute to them.
Your Growth Path
This is a development role. You will start by contributing to existing tools and learning the stack, the team, and real consulting delivery, and grow towards designing and shipping complete tools — web apps, APIs, and agent integrations — independently. We expect you to grow into the full breadth of the work over time, with mentorship throughout; we are not expecting you to arrive with all of it. How quickly you progress will depend on you, and we will support that growth rather than hold it to a fixed timetable.
Key Responsibilities
Consulting Tool Development
- Design and build AI-powered tools supporting consulting delivery — including data mapping, validation, documentation generation, and workflow automation.
- Progress towards building complete, deployable tools — web apps, APIs, and agent integrations — as your skills develop.
- Translate consultant problems into working solutions, developing your ability to scope and frame engineering challenges independently over time.
- Support the progression of solutions from MVP to stable, reusable tools.
- Learn to write robust, understandable, and maintainable code.
- Apply reuse principles — solutions must work across clients, not single engagements.
Engineering & Integration
- Integrate AI capabilities with SimCorp tools, workflows, APIs, enterprise systems, and data pipelines.
- Build an understanding of real client environments and delivery constraints.
- Participate in code reviews as both reviewer and reviewee.
- Work within defined delivery governance and contribute to standardization.
AI Engineering
- Apply prompting strategies, retrieval-augmented generation (RAG), and evaluation approaches.
- Learn to design AI behavior that is predictable, testable, and safe.
- Contribute to evaluation frameworks that validate output quality.
- Develop awareness of LLM limitations (hallucination, sensitivity, consistency).
Research & Continuous Learning
- Stay current with the rapidly evolving AI landscape — new models, tools, techniques, and frameworks.
- Read and digest research papers, technical blogs, and vendor releases, and share relevant findings with the team.
- Evaluate emerging tools and approaches for their practical fit with consulting delivery.
- Run small experiments and proofs-of-concept to test new capabilities before adopting them.
- Attend conferences, webinars, and industry events, and feed learnings back into the team's practices.
Collaboration & Community
- Build relationships and collaborate with AI engineers and technical teams across SimCorp.
- Share approaches, reusable components, and lessons learned with the wider internal AI community.
- Contribute to and draw on cross-team practices, standards, and tooling.
- Participate in internal AI forums, guilds, and communities of practice.
Delivery Support & Adoption
- Support teams during active tool usage and identify issues.
- Participate in feedback loops with consultants.
- Contribute to iterative refinement based on real-world use.
Knowledge & Reuse
- Contribute to internal toolsets and the accelerator library.
- Document solutions, patterns, and lessons learned.
- Participate in internal training and capability development.
Required Qualifications
What a strong candidate genuinely brings on day one.
We don't expect any candidate to meet every point below. If you have a strong core and are excited to grow into the rest, we encourage you to apply.
Core Engineering
- A recent (or final-year) BSc or MSc in Computer Science, Software Engineering, or a related field.
- A solid foundation in Python.
- Some experience building and consuming REST APIs.
- Exposure to web development — frontend and/or backend (e.g. React/TypeScript, or Python/Node back ends).
- Familiarity with Git and collaborative workflows (branches, pull requests, code review).
- Exposure to a cloud platform (Azure or AWS).
AI & LLM Exposure
- Hands-on exposure to developing AI solutions through coursework, projects, or internships.
- Awareness of prompt engineering and RAG approaches.
- An understanding of LLM behaviour and associated risks.
Mindset & Problem Solving
- Genuine curiosity about AI and a habit of self-directed learning — the field moves fast, and staying current is part of the job.
- Ability to break down ambiguous problems.
- Comfortable asking for help and incorporating feedback.
- A focus on building solutions that deliver real value.
Collaboration & Fit
- Enthusiasm for working openly — sharing knowledge and building relationships across teams, rather than working in isolation.
- Interest in applying technology in real-world delivery contexts.
- Ability to communicate with non-engineers.
- Openness to iteration and feedback.
Preferred Qualifications
Nice to have, but not expected — these are areas you will grow into with support.
- Building and deploying full-stack web applications end-to-end.
- Agentic / tool-using AI, and emerging interoperability protocols such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) — building tools that connect AI models to external systems, data, and other agents.
- Containerisation (Docker) and exposure to CI/CD.
- Relational and/or vector databases (e.g. PostgreSQL, SQLite, a vector store).
- Designing evaluation frameworks for AI output quality.
- Personal projects or open-source contributions demonstrating delivery capability — ideally something others have used.
- Exposure to enterprise or financial systems.
- Experience working in team environments (including academic projects).
Success is measured by growth, contribution, and the real adoption of delivered tools.
Benefits
SimCorp offers a strong work-life balance and structured opportunities for professional development. Benefits may vary by location across our 30+ global offices.
For this role, we provide:
- A defined mentorship programme.
- A peer learning environment.
- Structured development towards independent contribution.
- Dedicated time for research, learning, and staying current with AI developments.
