AI Platform & LLMOps Expert (Principal / Lead)

Emergesoft is working for an international FinTech & Telecom group building a world-class AI capability to power next-generation financial services across multiple markets. The client operates high-volume transactional platforms with billions of mobile money accounts and transactions.

• Minimum 8–10 years in software, platform, cloud, or Site Reliability Engineering (SRE), including 2–3 years leading or guiding engineering teams.
• Proven hands-on experience building and running enterprise-grade cloud platforms, Infrastructure as Code, containers, and automated pipelines.
• Practical experience operating LLM or ML workloads in production, focusing on model evaluation, monitoring, tracing, and cost management.
• Strong proficiency with cloud platforms (AWS, Azure, GCP), Kubernetes/containers, Infrastructure as Code (Terraform, Ansible), and secrets management.
• Demonstrated experience with on-call shifts, incident response leadership, and root-cause analysis in high-availability environments.
• Exposure to regulated environments with formal change management, audit, and security requirements.

Nice to have:

• Recognized cloud, platform, or SRE certifications (e.g., AWS/Azure DevOps/Solutions Architect, CKA, TOGAF).
• Postgraduate qualification (Master's degree) in a technical field.
• Experience in FinTech, banking, or other high-volume transactional platforms.

• AI Platform & Release Mechanics: Build and maintain multi-environment AI platform infrastructure, CI/CD pipelines, container orchestration, and release mechanics.
• Model Operations & LLMOps: Manage production model lifecycle, including provisioning, dynamic routing, versioning, fallback strategies, latency optimization, and cost control.
• Evaluation & Quality Gates: Build and own evaluation harnesses, regression test suites, and pre-release quality gates ensuring model/agent safety and performance.
• Observability & Incident Leadership: Design full observability stack (monitoring, tracing, alerting) and hold production accountability including on-call and incident response.
• Application Interfaces & APIs: Deliver full-stack applications, developer interfaces, and APIs for consuming AI capabilities.