AI Engineer – Consulting Tools

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.