MLOps & DevOps Engineer

About Devoteam

Devoteam is committed to combining technology with strong human values to foster positive transformation. With over 25 years of technology expertise and presence in more than 18 countries across EMEA and beyond, the company drives innovation and human-centered solutions to create meaningful impact.

Company Culture and Values

• Fair and Courageous: Emphasis on fairness, courageous decision-making, open listening, and supporting employees’ growth.
• Ambition and Results: Driven by entrepreneurial spirit, dedication to performance, delivering profitable growth, and continuously creating value for clients.
• Learning and Innovation: Encouraging curiosity and constant innovation to overcome challenges and foster excellent partnerships.
• Caring and Sharing: Promoting teamwork, collaboration, and a supportive environment that nurtures talent and career development.

Core Values: Respect, Frankness, Passion.

Job Overview

We are looking for an experienced DevOps/MLOps Engineer who will act as a bridge between data science research and AI engineering to ensure effective and efficient software delivery lifecycle (SDLC). The role involves designing, deploying, and maintaining cloud infrastructure and automation pipelines enabling scalable AI platforms and RAG (retrieval-augmented generation) pipelines.

Key Responsibilities

• Design and oversee scalable cloud infrastructure on Google Cloud Platform (GCP) using Terraform, focusing on GKE clusters, firewalls, and network policies.
• Create and maintain full SDLC continuous integration and continuous delivery (CI/CD) pipelines leveraging GitHub Actions, integrating tools such as Renovate, Sonar for code quality, and Artifactory for artifact management.
• Enhance system performance and optimize costs across cloud environments.
• Develop and automate end-to-end machine learning pipelines on Vertex AI, particularly for RAG architectures and automate data ingestion into Qdrant vector databases.
• Implement evaluation workflows to monitor and improve performance of large language model (LLM) systems and agentic workflows.
• Manage automated deployment strategies including A/B and Canary deployments for ML models.
• Build monitoring and alerting systems to ensure production model and infrastructure reliability.
• Implement detection measures for data drift and model drift to maintain deployed model accuracy.
• Collaborate with security teams to guarantee compliance and data privacy throughout the ML lifecycle.
• Integrate observability solutions such as Langfuse, OpenTelemetry, and Prometheus for transparency and fault diagnosis in ML pipelines and LLM applications.
• Utilize distributed tracing and logging for performance tuning and bottleneck detection across microservices and agentic workflows.

Qualifications

• Minimum of 3 years of experience in DevOps, Site Reliability Engineering (SRE), or MLOps roles.
• Proficiency working with Google Cloud Platform (GCP).
• Strong expertise with Docker containerization and Kubernetes (GKE) management.
• Skilled in Python programming and automation tools including GitHub Actions, Renovate, Sonar, Artifactory, Argo CD, and Helm charts.
• Working knowledge of SQL and NoSQL databases and experience with data orchestration frameworks such as Airflow.

Preferred Skills

• Hands-on experience with Vertex AI, RAG pipelines, Qdrant, and large language model orchestration frameworks like LangChain or LlamaIndex.
• Participation or contributions to open-source DevOps or MLOps projects.
• Possession of relevant certifications such as AWS Certified DevOps Engineer or Certified Kubernetes Administrator (CKA).

Additional Information

• Associated Business Unit: Data & Intelligence
• Candidate Level: Mid to Senior