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- Tasks: Design and maintain observability solutions for financial services, ensuring system reliability and performance.
- Company: Join a leading financial services firm focused on innovative AI-driven solutions.
- Benefits: Enjoy remote work flexibility and competitive day rates.
- Why this job: Be at the forefront of technology, collaborating with diverse teams in a dynamic environment.
- Qualifications: 10+ years of experience with OpenTelemetry and strong AI skills required.
- Other info: Contract role with potential for exciting projects in observability and machine learning.
The predicted salary is between 60000 - 84000 £ per year.
Join a Financial Services Leader as an AI-Driven Observability Engineer! We are seeking a highly skilled AI-Driven Observability Engineer to design, implement, and maintain end-to-end observability solutions for a leading financial services client. This role will be key in monitoring, logging, and tracing infrastructure and applications to ensure reliability, performance, and scalability.
If you have 10+ years of experience, strong OpenTelemetry expertise, and a passion for AI-driven observability, we want to hear from you!
Key Responsibilities- Build and enhance telemetry pipelines for logs, metrics, and traces using OpenTelemetry, Kafka, and Splunk
- Implement observability best practices across applications, platforms, and infrastructure
- Develop machine learning models for anomaly detection, predictive failure analysis, and root cause analysis
- Create Real Time monitoring dashboards and alerts for proactive troubleshooting
- Integrate observability solutions with event-driven architectures (Kafka, RabbitMQ, Pulsar)
- Collaborate with SREs, DevOps, and Engineering teams to improve system reliability
- Ensure observability data meets security and compliance standards (GDPR, HIPAA, etc.)
- Expertise in OpenTelemetry (Must-have!)
- Experience with observability tools like Prometheus, Grafana, ELK Stack, Loki, Jaeger, Zipkin
- Strong knowledge of Python, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy for AI-driven observability
- Hands-on experience with Apache Airflow, Apache NiFi, Spark for ETL and data processing
- Familiarity with AWS, Azure, or GCP observability services (eg, CloudWatch, Azure Monitor)
- Experience with Kubernetes, Docker, and Service Mesh technologies (Istio, Linkerd)
- Understanding of event-driven architectures and messaging systems (Kafka, RabbitMQ)
- Knowledge of time-series databases (InfluxDB, VictoriaMetrics) and SQL/NoSQL databases
- Experience in AIOps and intelligent observability
- Knowledge of Chaos Engineering for resilience testing
- Relevant certifications in AWS, Azure, Kubernetes, or Observability tools
- Exposure to big data technologies like Hadoop, Spark, Flink
- Machine learning experience for predictive observability
- Work with a top financial services company on cutting-edge observability solutions
- Automate infrastructure monitoring and improve system resilience
- Collaborate with cross-functional teams in a fast-paced environment
AI-Driven Observability Engineer employer: WNTD
Contact Detail:
WNTD Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI-Driven Observability Engineer
✨Tip Number 1
Network with professionals in the observability and AI fields. Attend relevant meetups, webinars, or conferences to connect with others who work in similar roles. This can help you gain insights into the industry and potentially lead to referrals.
✨Tip Number 2
Showcase your expertise in OpenTelemetry and other required tools by contributing to open-source projects or writing articles. This not only demonstrates your skills but also helps you build a portfolio that stands out to potential employers.
✨Tip Number 3
Prepare for technical interviews by practising common questions related to observability, machine learning, and the specific tools mentioned in the job description. Consider mock interviews with peers to refine your responses and boost your confidence.
✨Tip Number 4
Stay updated on the latest trends and advancements in AI-driven observability. Follow industry leaders on social media, subscribe to relevant newsletters, and engage in discussions to demonstrate your passion and knowledge during interviews.
We think you need these skills to ace AI-Driven Observability Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your 10+ years of experience and expertise in OpenTelemetry. Include specific projects or achievements that demonstrate your skills in AI-driven observability and relevant tools like Kafka, Splunk, and Prometheus.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI-driven observability. Mention how your background aligns with the key responsibilities of the role, such as building telemetry pipelines and developing machine learning models for anomaly detection.
Highlight Relevant Skills: In your application, emphasise your hands-on experience with tools and technologies mentioned in the job description, such as Python, TensorFlow, and Kubernetes. This will help you stand out as a strong candidate.
Showcase Collaboration Experience: Since the role involves working with SREs, DevOps, and Engineering teams, include examples of past collaborations in your application. Highlight how you contributed to improving system reliability and implementing observability best practices.
How to prepare for a job interview at WNTD
✨Showcase Your OpenTelemetry Expertise
Since expertise in OpenTelemetry is a must-have for this role, be prepared to discuss your experience in detail. Highlight specific projects where you've implemented telemetry pipelines and how they improved observability.
✨Demonstrate Your Problem-Solving Skills
The role involves developing machine learning models for anomaly detection and predictive analysis. Be ready to share examples of how you've tackled complex problems in the past, particularly in relation to observability and system reliability.
✨Familiarise Yourself with Relevant Tools
Make sure you know the ins and outs of tools like Prometheus, Grafana, and ELK Stack. Discuss your hands-on experience with these tools during the interview, as well as any challenges you faced and how you overcame them.
✨Understand Event-Driven Architectures
Since the job requires integrating observability solutions with event-driven architectures, brush up on your knowledge of Kafka, RabbitMQ, and similar technologies. Be prepared to explain how you've used these systems in previous roles.
