6 publicaciones de empleo destacadas de data engineer en elda alicante provincia (¡Están contratando!)

Data Engineer
ESML SD Iberia Holding S A U —Los Montoyos, Alicante provincia
- Jornada completa
- Azure
- DevOps
- Inglés
Solicitud rápida
Devops Engineer
ESML SD Iberia Holding S A U —Los Montoyos, Alicante provincia
- Jornada completa
- DevOps
- Inglés
- Java
Solicitud rápida
AI Engineer
ESML SD Iberia Holding S A U —Los Girasoles (San Vicente Del Raspeig), Alicante provincia
- Jornada completa
- Azure
- DevOps
- Máster
Solicitud rápida
Senior Quality Assurance
ESML SD Iberia Holding S A U —Los Montoyos, Alicante provincia
- Jornada completa
- Trabajo híbrido
- Español
- Inglés
- Oficina
Solicitud rápida
Technical Project Engineer
Clinisys —La Solana, Alicante provincia
- De 30.446 € a 45.668 € al año
- Teletrabajo
- Español
- Oracle
- Experiencia en laboratorio
Business Intelligence Engineer
Verisure Innovation AB —Los Girasoles (San Vicente Del Raspeig), Alicante provincia
- Jornada completa
- SQL
- Agile
Solicitud rápida

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Data Engineer

ESML SD Iberia Holding S A U -
Los Montoyos, Alicante provincia

Solicitud rápida

Detalles del empleo

Jornada completa

Cualificaciones

CI/CD
Azure
Derecho
DevOps
Inglés
Máster
Kernel
AWS
Docker
Machine learning
Terraform
NLP
GitHub
Escalabilidad
Cloud
Inteligencia artificial
Jenkins
GitLab
Python

Descripción completa del empleo

We are looking for a MLOps / AIOps / LLMOps / AgentOps 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, DevOps, 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 / AgentOps 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 AgentOps 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 (GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar)
Familiarity with observability and monitoring concepts (CloudWatch, OpenTelemetry, 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 SageMaker, Azure ML, MLflow, Kubeflow
Exposure to Speech Analytics technologies (ASR, diarization, conversational NLP)
Experience with cloud cost optimization / FinOps, especially for AI workloads
Experience building or operating AI agents, copilots, or conversational systems
Familiarity with LLM frameworks (LangChain, LlamaIndex, 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: GitHub Actions, GitLab CI, Azure DevOps
Observability: Prometheus, Grafana, ELK/EFK, OpenTelemetry
Infrastructure as Code: Terraform, Bicep, CloudFormation
AI / ML Tools: MLflow, Azure ML, SageMaker, LangChain, LlamaIndex, Semantic Kernel
Primary Language: Python

Solicitud rápida

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