Correlation & Causality Engineer - AI Observability

Avaloq Group; Makati; Hybrid; PHP 1,500,000 - 2,300,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

AVALoq Group in the Philippines (Makati) is seeking a Correlation and Causality Engineer to design AI-driven observability insights from logs, metrics and traces. You will build production-ready services in Java and Python, and deploy AI models on local/on-prem infrastructure, using Prometheus, Loki and Grafana.

Join a team delivering data-driven solutions with LLMs, RAG, and vector stores, collaborating across DevOps, SRE, product and operations to turn complex data into actionable knowledge.

Qualifications

• 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 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).
• 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.
• 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.

Responsibilities

• Correlation, Causality and Inference Solutions: design and implement solutions that link events, metrics, logs and traces into meaningful insights.
• Data-Driven Models: design, train, evaluate and maintain ML and statistical models built on observability data.
• Productionization: develop robust, scalable services in Java and Python, taking models from prototype to production.
• AI and LLM Solutions: design and implement LLM-based applications and deploy models on local/on-prem infrastructure.
• Quality and Evaluation: define metrics to evaluate accuracy, reliability and explainability of AI-based solutions.
• Observability Data and Collaboration: explore observability stack to understand data quality and collaborate with DevOps, SRE, product, and operations teams.

Skills

Java

Python

AI / ML

LLMs

LangChain

Genome?

Docker

Kubernetes

Git

CI/CD

Prometheus

OpenTelemetry

Grafana

Terraform

Cloud (AWS/Azure/GCP)

Education

Bachelor’s degree in Computer Science, Engineering, Data Science or related field

Tools

LangChain

Ollama

vLLM

Hugging Face

Embeddings / Vector stores

Jenkins

GitLab / GitHub

Docker

Kubernetes

Job description

AVALoq Group in the Philippines (Makati) is seeking a Correlation and Causality Engineer to design AI-driven observability insights from logs, metrics and traces. You will build production-ready services in Java and Python, and deploy AI models on local/on-prem infrastructure, using Prometheus, Loki and Grafana.

Join a team delivering data-driven solutions with LLMs, RAG, and vector stores, collaborating across DevOps, SRE, product and operations to turn complex data into actionable knowledge.