Job details:

• Project Duration: 15 June 2026 – 30 June 2027
• Work Mode: Remote, with optional 1–2 days on-site in Madrid or Barcelona
• Experience Required: 3+ years

Scope:

We are seeking a Machine Learning Engineer focused on production deployment (MLOps) to operationalize, deploy, monitor, and maintain AI models in a corporate environment using IBM Cloud / Kubernetes (IKS).

The role acts as a technical bridge between Data Science, Software Engineering, and business stakeholders, ensuring scalable, stable, and traceable AI services. The primary focus is Intelligent Document Management — OCR, classification, extraction, and document processing via AI microservices.

Key Responsibilities:

AI Model Deployment & Production Support

• Deploy pre-trained ML/AI models into production.
• Implement inference pipelines for batch and real-time processing.
• Monitor and maintain model performance, latency, and resource consumption.
• Diagnose issues and troubleshoot production incidents.

Python Backend & Microservices Development

• Develop “production-safe” Python services, including asynchronous code and concurrent processing.
• Integrate Data Science SDKs and model serving frameworks (e.g., MLServer, FastAPI).
• Ensure code scalability, reliability, and observability.

Kubernetes & IBM Cloud Operations

• Configure and manage pods, deployments, services, ConfigMaps, secrets, resource limits, and health probes.
• Implement horizontal scaling (HPA) and optimize worker parallelism.
• Deploy and monitor AI microservices using IBM Kubernetes Service (IKS), Object Storage, and Log Analysis tools.

Observability & Monitoring

• Use Grafana to monitor CPU, memory, throughput, and latency.
• Utilize IBM Log Analysis / LogDNA for structured logging, debugging, and anomaly detection.
• Define and track key operational metrics.

Intelligent Document Management

• Apply AI for OCR, document classification, data extraction, and format conversion (PDF, Office, images).
• Translate business requirements into technical workflows.
• Handle edge cases such as corrupt, scanned, or high-volume documents.

Cross-Functional Collaboration

• Work closely with Data Science to integrate models into production.
• Collaborate with Software Engineering to define APIs, ensure SLAs, and integrate with backend systems.
• Translate business requirements into technical specifications and measurable outcomes.

Dev Environment & Tools

• Work in Windows-based environments using Python, Docker, VS Code / PyCharm.
• Replicate Kubernetes stacks locally for testing and debugging.
• Use Git, virtual environments, and unit/integration testing frameworks.

Soft Skills & Professional Traits

• Operational mindset: production-first approach.
• Strong analytical and problem-solving skills under pressure.
• Clear communication with technical and non-technical stakeholders.
• Autonomous, responsible, and focused on stability and reliability.

Skills:

• Python: Advanced backend programming, async, concurrency, production-safe coding.
• ML/AI Knowledge: Deployment of classification, OCR, extraction, NLP models; understanding inference pipelines, batch vs real-time inference.
• Kubernetes: Medium-to-advanced experience in deploying microservices, scaling, resource management, health checks.
• IBM Cloud: Familiarity with IKS, Cloud Object Storage, Log Analysis / LogDNA.
• Microservices & APIs: REST API development, versioning, integration with backend services.
• Observability & Monitoring: Grafana, structured logging, performance metrics analysis.
• Document AI / Domain Knowledge: OCR, classification, extraction, data quality awareness

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