AI Operation Lead

Descripción del puesto:

The AI Operations Lead contributes hands-on to integration, deployment and monitoring of AI systems such as ML, GenAI, RAG and agentic workflows. The role focuses on a subset of products/services and ensures operational excellence, observability, and performance.

Key duties and responsibilities

* Implement and operate integration of AI capabilities into enterprise products following standard patterns
* Contribute to deployment of:
* RAG pipelines
* Copilots and AI assistants
* Agentic workflows
* Predictive ML services
* Support delivery squads in integrating AI services into business applications
* roubleshoot and resolve integration or runtime issues in production

AI Observability & Monitoring (Core focus)
* Design and implement AI observability frameworks, including:
* Model performance monitoring (drift, quality, hallucination signals)
* Usage and adoption metrics
* Latency, reliability, and system health
* Ensure proper logging, tracing, and monitoring of AI pipelines
* Contribute to definition of AI SLAs/SLOs aligned with business expectations
* Support incident management and post-mortem analysis for AI systems

Cost & Performance Optimization
* Monitor AI-related cloud consumption and inference costs
* Optimize pipelines for efficiency (model selection, caching, orchestration)
* Contribute to FinOps practices specific to AI workloads

Business Acumen

* Understands operational impact of AI systems on business processes
* Able to balance performance, cost, and quality trade-offs
* Communicates effectively with technical and business stakeholders

Requerimientos del candidato/a:

Required experience & competencies
* 5-8 years in software/ML engineering
* Cloud (Azure), Kubernetes, Python
* Experience with GenAI and ML systems

Technical Skills
* Strong hands-on experience in:
* Python, APIs, microservices architecture
* Cloud environments (Azure preferred, AWS/GCP acceptable)
* Kubernetes and containerized deployments
* Experience with:
* MLOps / LLMOps tooling
* Monitoring/observability tools (e.g., logs, metrics, tracing)
* Data pipelines and distributed systems
* Understanding of:
* GenAI / LLM systems (RAG, embeddings, prompting)
* ML lifecycle and deployment patterns

Soft skills
* Hands-on and problem-solving mindset
* Ability to debug complex AI systems in production
* Strong collaboration with engineering and product teams
* Ability to explain technical issues clearly to non-experts
* Proactive and continuous improvement mindset

Business acumen
* Can adapt his/her speech to make relevant for business users
* Can interact effectively with top management
* Can support in produce presentations or architecture material
Required Education
* Master's degree (Ph. D. is a plus) in Science, Technology, Engineering, Computer Science,
* Bachelor's degree plus ASA or similar work experience is accepted in place of a relevant Master's degree
* Certifications on Cloud or Microservices or Kubernetes (CKAD) are plus