Experienced AI/ML Engineer (Clearing)

JOIN OUR TEAM At Levi Nine we are passionate about what we do. We love our work and together in a team we are smarter and stronger. We work in a dynamic and challenging environment with talented and forward-thinking people who are part of creative and innovative teams. We are looking for skilled team players who make change happen. Are you one of these players?

OUR PARTNER: Our partner, ABN AMRO Clearing, is a global clearing firm providing access to major financial markets worldwide. They combine financial resilience with technological innovation to support their clients in highly regulated environments.

IT is at the core of their organization, with more than 30 product teams and 10 platform teams focused on building high-quality products and services for their customers.

AI is becoming a key driver of innovation, with ongoing initiatives to embed AI into core processes to improve efficiency, reduce risk, and enable better decision-making.

THE ROLE INVOLVES: As an Experienced AI/ML Engineer, you design and build production-grade AI systems, working at the intersection of engineering, data, and business use cases.

You will focus on modern AI architectures, including LLM-powered systems, agent-based workflows, retrieval mechanisms, and evaluation pipelines, contributing to scalable and reliable AI solutions.

This role offers the opportunity to shape how AI is applied in a real-world, high-impact environment, with direct influence on architecture, design decisions, and best practices.

Responsibilities: Core AI Engineering

• Design and implement LLM-based applications and agent workflows (tool usage, orchestration, context handling).
• Build and optimize retrieval and memory systems (vector search, embeddings, session and long-term memory).
• Develop robust evaluation pipelines (automated + human evaluation, benchmarking, quality tracking).
• Integrate AI services with cloud platforms (e.g., AWS Bedrock) and enterprise systems.
• Ensure performance, scalability, and reliability of AI systems in production.

Data & AI Lifecycle

• Build and maintain data flywheels (instrumentation, dataset curation, continuous improvement loops).
• Support fine-tuning strategies (e.g., LoRA, prompt engineering, evaluation-driven refinement).
• Translate business problems into AI-driven solutions and architectures.

Architecture & Best Practices

• Contribute to AI architecture decisions (tooling, frameworks, integration patterns).
• Establish standards for evaluation, testing, and deployment of AI systems.
• Improve observability and monitoring of LLM-based solutions (quality, latency, cost).

Collaboration

• Work closely with business stakeholders and product teams to deliver fit-for-purpose AI use cases.
• Collaborate with engineers and data teams to ensure end-to-end solution integration.

TECHNICAL PLAYGROUND:

• 5+ years of experience in software engineering / ML engineering

• Strong experience with Python and building production systems

• Hands-on experience working with LLMs (via APIs or SDKs)

• Experience building agent-based or multi-step AI workflows

• Experience with cloud AI platforms (e.g., AWS Bedrock or similar)

• Strong understanding of:

• Retrieval-Augmented Generation (RAG)

• Embeddings and vector databases

• Evaluation methodologies for LLM systems

• Experience deploying AI systems in production environments

• Understanding of system design, scalability, and performance optimization

NICE TO HAVE:

• Experience in regulated environments (e.g., financial services)
• Exposure to fine-tuning techniques and model optimization
• Familiarity with modern AI tooling ecosystems (LangChain, LlamaIndex, etc.)
• Relevant university degree (Computer Science, AI, or similar)
• Experience with AI platform ecosystems and tooling
• Interest in emerging AI trends and technologies

SOFT SKILLS:

• Strong analytical thinking and structured problem-solving skills
• Excellent verbal and written English level - clear and effective communication skills
• Entrepreneurial/Ownership mindset and proactive approach
• Ability to collaborate closely with business stakeholders
• Continuous learning mindset, staying up to date with AI advancements