MLOps Engineer

About the role

Promtior, an Agentic AI company, is looking for an MLOps Engineer to join its Engineering team as the senior technical reference for ML and AI infrastructure. This is a hands-on architecture and leadership role, working embedded with a global fintech client: you will shape how every model and AI component is trained, deployed, monitored and run in production, enabling Data Science and AI teams to move from idea to production reliably, observably and compliantly.

Key responsibilities

• Define and evolve the end-to-end ML platform architecture (data, training, model registry, serving, monitoring and governance) used by multiple squads, and set standard patterns such as reproducible training pipelines, experiment tracking, model packaging, versioning and promotion flows, and safe online and batch inference rollouts (canary, shadow, rollback).
• Balance reliability, performance and cost for ML workloads, working closely with SRE, Infrastructure and FinOps.
• Review and challenge the design of new models and data pipelines against platform standards and non-functional requirements.
• Partner with product squads such as Fraud and Anomaly Detection to set SLAs and SLOs, logging, metrics and alerting, and strengthen on-call readiness through playbooks, dashboards and incident reviews.
• Use agents and AI services to automate MLOps: feature store operations, training and evaluation pipelines, promotion gates, runbook generation, alert triage, incident enrichment and cost optimization.
• Define infrastructure, contracts and guardrails for consuming AI services safely, and contribute to their evaluation, observability and safety (prompts, policies, PII redaction, auditability).
• Set standards for model and data access control, PII handling, auditability of model changes and environment separation, working with InfoSec and Architecture.
• Mentor MLOps and Data/ML engineers on system design, reliability, observability and CI/CD for ML, and lead design and architecture reviews.

Required profile

• Solid experience owning or designing MLOps platforms or ML infrastructure used by multiple teams.
• Strong hands-on experience building and operating production ML workloads on Databricks, including Feature Store (ideally with Unity Catalog), MLflow experiment tracking and model lifecycle management, and batch and real-time deployment.
• Experience building production-grade ML pipelines: experiment tracking, reproducible training, model registry, CI/CD for models and data pipelines, and online and batch inference at scale.
• Ability to communicate with technical and non-technical stakeholders, translating infrastructure and AI/ML trade-offs into business language.

Required skills

• Distributed systems and data/stream processing (Spark, Flink or similar)
• Familiarity with cloud-based ML platforms (SageMaker, Vertex AI or equivalent) and container-based deployments
• Observability for ML systems: metrics, logs, traces, data and model drift, freshness and quality checks
• Especially valued: AI assistants in engineering orgs, LLM/AI infrastructure (gateways, vector stores, evaluation harnesses), prior experience as Technical Referent, Tech Lead or Architect, and contributions to internal standards or tech communities

What we offer

• 20 paid days off per year
• Hybrid work model
• Flex days for monthly team activities
• Professional development courses
• Internal clubs (soccer, padel, running, cinema)