Correlation and Causality Engineer

Founded and headquartered in Switzerland, Avaloq is continuously expanding its global footprint with around 2,500 colleagues in 10 countries, and more than 160 clients in 35 countries. We are an industry-leading provider of wealth management technology and services for financial institutions around the world, including private banks and wealth managers, investment managers, as well as retail and neo banks. Our research led approach and continual innovation is powered by the passion and creativity of our colleagues.

We are always looking for talented people to join us on our mission to orchestrate the financial ecosystem and democratize access to wealth management. Avaloq offers the opportunity to work closely with some of the world’s leading financial institutions as we jointly develop and shape careers. Championing a collaborative, supportive and flexible work environment empowers our colleagues to reach their full potential.

As a Correlation and Causality Engineer you will be part of a team that is responsible for designing and implementing solutions that provide insights, using observability data, to the entire Avaloq. You will combine software engineering and applied AI to turn large volumes of logs, metrics and traces into actionable knowledge: detecting correlations, inferring root causes and anticipating incidents before they impact our clients.

You need to deeply understand how Avaloq works, how the observability stack is operated, how data is collected and how it can be used. This role requires strong technical expertise and hands-on experience in software development and AI, mainly to develop correlation and inference solutions, including data-driven models and LLM-based applications that run on our own infrastructure.

Your key tasks

Correlation, Causality and Inference Solutions

• Design and Implement: build correlation, root-cause analysis and inference solutions that link events, metrics, logs and traces across the Avaloq landscape, turning raw observability data into meaningful insights
• Data-Driven Models: design, train, evaluate and maintain machine learning and statistical models (e.g. anomaly detection, time-series analysis, causal inference) built on observability data
• Productionization: develop robust, scalable and maintainable services in Java and Python, taking models from prototype to production

AI and LLM Solutions

• LLM-Based Applications: design and implement solutions leveraging Large Language Models and related tooling (e.g. RAG, agents, tool/function calling, embeddings, vector stores) to support incident analysis, troubleshooting and knowledge discovery
• Local AI: select, deploy, fine-tune and operate AI models and LLMs on local/on-prem infrastructure, ensuring performance, cost-efficiency, security and data privacy
• Quality and Evaluation: define metrics and processes to evaluate the accuracy, reliability and explainability of AI-based solutions, and continuously improve them

Observability Data and Collaboration

• Data Exploration: work with the observability stack (Prometheus, OpenTelemetry, Loki, Grafana) to understand what data is available, its quality and how it can be best exploited
• Internal Consultancy: interact with teams across the company (DevOps, SRE, application development, product and operations) to understand their needs and identify where correlation, causality and AI can deliver the most value
• Stakeholder Engagement: communicate complex technical insights clearly to stakeholders, enabling informed decision-making and prioritization of new use cases

Engineering Excellence

• Software Engineering Practices: apply high engineering standards, including code reviews, testing, documentation and version control with Git
• CI/CD and Automation: build and maintain CI/CD pipelines (e.g. Jenkins, GitLab/GitHub) for the automated build, test, deployment and monitoring of applications and models
• Operations: deploy and run solutions on containerized platforms, and contribute to the reliability, monitoring and continuous improvement of what you build

Technical Skills

• Bachelor’s degree in Computer Science, Engineering, Data Science or a related field, or equivalent experience may be considered
• Strong software development skills in Java and Python, with solid knowledge of software design, testing and engineering best practices
• Strong expertise in AI, with proven experience in building data-driven models (machine learning, statistical modeling, time series analysis, anomaly detection) and a good understanding of correlation and causal inference techniques
• Hands-on experience with LLMs and LLM-related tooling (e.g. prompt engineering, RAG, embeddings and vector databases, agent and tool-calling frameworks such as LangChain or similar)
• Experience running and fine-tuning AI models locally/on-prem (e.g. with Ollama, vLLM, Hugging Face or similar), including a good understanding of GPU/resource requirements, model selection and optimization
• Proficiency with common data and ML libraries and frameworks (e.g. pandas, NumPy, scikit-learn, PyTorch or TensorFlow)
• Solid DevOps skills: Git, CI/CD pipelines and automation tools like Jenkins and GitLab/GitHub
• Good working knowledge of containerized platforms such as Docker and Kubernetes
• Some experience with observability stacks and standards such as Prometheus, OpenTelemetry, Loki and Grafana, with a good understanding of logs, metrics and traces
• Familiarity with infrastructure as code (e.g. Terraform) and with cloud platforms such as AWS, Azure, OCI or GCP is a plus
• Ability to analyze data, work with stakeholders to understand business and operational needs, and translate them into concrete AI-driven solutions

Soft Skills

• Good problem-solving skills with a focus on delivering high-quality, scalable solutions
• Effective communication skills, both written and verbal, with the ability to convey complex technical concepts to technical and non-technical stakeholders.
• Ability to work in a fast-paced, collaborative environment with changing priorities

We realize that managing work life balance is a challenge we all face in our daily lives and in order to support with this we are pleased to offer hybrid and flexible working for most of our Avaloqers to maintain work life balance and still continue our fantastic Avaloq culture in our global offices.

In Avaloq we are proud to embrace diversity and understand the success of our business is built on the power of different opinions, we are whole heartedly committed to fostering an equal opportunity environment and inclusive culture where you can be your true authentic self.

We hire, compensate and promote regardless of origin, age, gender identity, sexual orientation or any other fantastic traits that make us all unique, we have done our best to write this advert in an inclusive and neutral way.

Please be aware that we will not accept speculative CV submissions for any of our roles from recruitment agencies, and any unsolicited candidate submissions will be exempt from any payment expectations.