We are looking for a
MLOps / AIOps / LLMOps / Agent Ops Engineer
to join a multidisciplinary Data & AI team. The main mission of this role is to
design, operate, and continuously evolve our AIOps platform
, ensuring that our AI products run in a
reliable, scalable, and cost?efficient
way.
This position is
strongly focused on platform, infrastructure, automation, observability, and operations
rather than on building ML models or AI products themselves.
You will work with modern cloud technologies (mainly
AWS
, with some
Azure
exposure) and collaborate closely with
Data Scientists, Data Engineers, and Product teams
to bring AI solutions into production and keep them running smoothly.
We are open to candidates with
strong expertise in at least one core area
(e.g. cloud, Dev Ops, platform engineering, or ML operations) and
solid foundational knowledge in the others
, with motivation to grow across the full AI operations stack.
Key Responsibilities
Design, maintain, and evolve the AIOps platform
supporting:
Traditional machine learning models in production
LLM?based solutions such as
RAG pipelines and AI Agents
Speech Analytics
use cases (ASR, conversation analysis, NLP)
Build and operate ML and LLM pipelines
with a strong focus on:
Reliability, automation, and observability
Model and LLM quality, performance, and drift monitoring
Cloud cost control and optimization
Implement LLMOps / Agent Ops practices
, including:
LLM evaluation and observability
Prompt management, traceability, and specialized logging
Agent integration, orchestration, and lifecycle management




Ensure continuous operation of AI products
, including:
Alerts, dashboards, SLOs / SLIs
Scalability strategies and basic auto?remediation mechanisms
Manage deployments in cloud environments
(AWS / Azure) and container platforms (Docker / Kubernetes)
Collaborate closely with Data Scientists and Data Engineers
to productionize robust, scalable AI solutions
Contribute to internal standards, automation, and best practices
across the AI and data ecosystem
Required Skills (Must Have)
Hands?on experience in
MLOps, AIOps, or operating ML systems in production
Solid understanding of
LLMOps and Agent Ops concepts
(RAGs, agents, evaluation, monitoring)
Experience working with
AWS and/or Azure
in production environments
Practical knowledge of
containers and Kubernetes
(Docker, basic Helm usage, etc.)
Experience with
CI/CD pipelines
(Git Hub Actions, Git Lab CI, Azure Dev Ops, Jenkins, or similar)
Familiarity with
observability and monitoring concepts
(Cloud Watch, Open Telemetry, Prometheus, etc.)
Experience managing infrastructure as code (
Terraform, Bicep, CDK, or similar
)
Python
experience and familiarity with the ML ecosystem (e.g. scikit?learn, PyTorch), even if not a Data Scientist
Good understanding of the
ML / LLM lifecycle




, from development to production and monitoring
Fluent English
to work in an international environment
Nice to Have (Not Required, but Valuable)
Experience with ML/AI platforms such as
Sage Maker, Azure ML, MLflow, Kubeflow
Exposure to
Speech Analytics technologies
(ASR, diarization, conversational NLP)
Experience with
cloud cost optimization / Fin Ops
, especially for AI workloads
Experience building or operating
AI agents, copilots, or conversational systems
Familiarity with
LLM frameworks
(Lang Chain, Llama Index, Semantic Kernel, etc.)
Experience with
workflow and orchestration tools
(Airflow, Argo, Step Functions, Durable Functions)
Professional Skills & Mindset
Strong focus on
reliability, automation, and scalability
Ability to collaborate effectively in
multidisciplinary teams
Clear communication and documentation?oriented mindset
Platform mindset
: building reusable, maintainable, and robust solutions
Proactive, analytical, and continuous?improvement driven
Strong sense of
ownership and end?to?end responsibility
Motivation to
learn and grow across the AI operations stack
Technology Environment
Cloud
: AWS, Azure
Orchestration & Containers
: Kubernetes, Docker
CI/CD
: Git Hub Actions, Git Lab CI, Azure Dev Ops
Observability
: Prometheus, Grafana, ELK/EFK, Open Telemetry
Infrastructure as Code
: Terraform, Bicep, Cloud Formation
AI / ML Tools
: MLflow, Azure ML, Sage Maker, Lang Chain, Llama Index, Semantic Kernel
Primary Language
: Python

📌 Data Engineer (España)
🏢 Verisure
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