The world of payment processing is rapidly evolving, and businesses are looking for loyal and strategic partners to help them grow.

Meet Nuvei, Nuvei is the global fintech building the infrastructure for every payment, everywhere. Its modular, flexible, and scalable technology enables leading companies to accept next-generation payments, offer all payout options, and benefit from card issuing, banking, risk, and fraud management services. Connecting businesses to their customers in more than 200 markets, with local acquiring in 52 markets, 150 currencies, and over 720 alternative payment methods, Nuvei provides the technology and insights for customers and partners to succeed locally and globally through one integration.

At Nuvei, we live our core values, and we thrive on solving complex problems. We’re dedicated to continually improving our product and providing relentless customer service. We are always looking for exceptional talent to join us on the journey!
We are always looking for exceptional talent to join us on the journey!

Your Mission

As an MLOps Engineer at Nuvei, your mission is to design, build, and operate the platforms that power our machine learning and generative AI products spanning real-time use cases such as large-scale fraud scoring, MCP & agentic workflows support. You’ll create reliable CI/CD for models and Agents, robust data/feature pipelines, secure model serving, and comprehensive observability. You will also support our agentic AI ecosystem and Model Context Protocol (MCP) services so that models can safely use tools, data, and actions across Nuvei.

You will partner closely with Data Scientists, Data/Platform Engineers, Product, and SRE to ensure every model from classic ML to LLM/RAG agents moves from prototype to production with strong reliability, governance, cost efficiency, and measurable business impact.

Responsibilities

Operate & Develop ML/LLM platforms on Kubernetes + cloud (Azure; AWS/GCP ok) with Docker, Terraform, and other relevant tools
Manage object storage, GPUs, and autoscaling for training & low-latency model serving
Manage cloud environment, networking, service mesh, secrets, and policies to meet PCI-DSS and data-residency requirements
Build end-to-end CI/CD for models/agents/MCP tooling (versioning, tests, approvals)
Deliver real-time fraud/risk scoring & agent signals under strict latency SLOs.
Maintain MCP servers/clients: tool/resource definitions, versioning, quotas, isolation, access controls
Integrate agents with microservices, event streams, and rule engines; provide SLAs, tracing, and on-call runbooks
Measure operational metrics of ML/LLM (latency, throughput, cost, tokens, tool success, safety events)
Enforce governance: RBAC/ABAC, row-level security, encryption, PII/secrets management, audit trails.
Partner with DS on packaging (wheels/conda/containers), feature contracts, and reproducible experiments.
lead incident response and post-mortems.
Drive FinOps: right-sizing, GPU utilization, batching/caching, budget alerts.

Qualifications:

4+ years in DevOps/MLOps/Platform roles building and operating production ML systems (batch and real-time)
Strong hands-on with Kubernetes, Docker, Terraform/IaC, and CI/CD
Practical experience with Spark/Databricks and scalable data processing
Proficiency in Python & Bash
Ability to operate DS code and optimize runtime performance.
Experience with model registries (MLflow or similar), experiment tracking, and artifact management.
Production model serving using FastAPI/Ray Serve/Triton/TorchServe, including autoscaling and rollout strategies
Monitoring and tracing with Prometheus/Grafana/OpenTelemetry; alerting tied to SLOs/SLAs
Solid understanding of PCI-DSS/GDPR considerations for data and ML systems
Experience with the Azure cloud environment is a big plus
Operating LLM/agent workloads in production (prompt/config versioning, tool execution reliability, fallback/retry policies)
Building/maintaining RAG stacks (indexing pipelines, vector DBs, retrieval evaluation, hybrid search)
Implementing guardrails (policy checks, content filters, allow/deny lists) and human-in-the-loop workflows
Experience with feature stores - Qwak Feature Store, Feast
A/B testing for models and agents, offline/online evaluation frameworks
Payments/fraud/risk domain experience; integrating ML outputs with rule engines and operational systems - Advantage
Familiarity with Databricks Unity Catalog, dbt, or similar tooling

Nuvei is an equal-opportunity employer that celebrates collaboration and innovation and is committed to developing a diverse and inclusive workplace. The team at Nuvei is comprised of a wealth of talent, skill, and ambition. We believe that employees are happiest when empowered to be their true, authentic selves. So, please come as you are. We can’t wait to meet you.

Benefits

Private Medical Insurance
Office and home hybrid working
Global bonus plan
Volunteering programs
Prime location office close to Tel Aviv train station

Our recruitment process may use automated tools, including AI, to support application management and candidate shortlisting.

Machine learning operations engineer