DevSecOps / AgentOps Engineer - M/F

DevSecOps / AgentOps Engineer - M/F

We are looking for a DevSecOps / AgentOps Engineer to join a cloud-native Data & AI platform dedicated to the industrialisation and production deployment of Data, Machine Learning, Generative AI and AI Agent use cases.

Working within a multidisciplinary team of Data Engineers, Data Scientists, ML Engineers and Software Engineers, you will be responsible for the industrialisation, deployment, observability, security and operational reliability of the platform and its AI workloads in production.

Key Responsibilities

You will be responsible for:

• designing, evolving and operating a platform built on Kubernetes;

• designing and automating CI/CD pipelines;

• implementing GitOps practices;

• automating infrastructure and environments through Infrastructure as Code;

• deploying and operating cloud / multi-cloud environments;

• embedding DevSecOps practices into build, testing and deployment pipelines;

• securing container images, dependencies, secrets, APIs and workloads;

• industrialising Data, ML, GenAI and AI Agent pipelines;

• implementing and operating monitoring, logging, metrics and distributed tracing;

• ensuring platform performance, availability, resilience and scalability;

• securing environments, identities and access management;

• continuously improving DevOps, DevSecOps, DataOps, MLOps and AgentOps practices.

• DevSecOps Scope

• You will integrate security directly into the development, deployment and operational lifecycle of the platform.

This includes:

• securing CI/CD pipelines;

• dependency and vulnerability scanning;

• container image security scanning;

• Kubernetes configuration and Infrastructure as Code security controls;

• secure secrets management;

• IAM and RBAC policies;

• implementation of least-privilege principles;

• API and workload security;

• automation of security controls;

• contribution to Security by Design and Shift-Left Security practices.

• AgentOps / GenAI Scope

• You will also be responsible for the industrialisation and operational management of AI Agents in production.

This includes:

• CI/CD and automated deployment of AI agents;

• versioning of models, prompts, configurations and components;

• supervision of AI agents and agentic workflows;

• observability of logs, traces, metrics and LLM calls;

• monitoring service quality and runtime performance;

• tracking latency, consumption and inference costs;

• implementing rollback mechanisms;

• implementing kill switch mechanisms and operational controls;

• securing agents, APIs, models and secrets;

• incident management and production operations;

• contributing to the reliability and resilience of agentic systems.

• Target Technical Environment

• Platform & Orchestration

• Kubernetes, Docker, Helm

• CI/CD & GitOps

• GitLab CI / GitHub Actions / Jenkins, Argo CD / Flux

• Infrastructure as Code

• Terraform, Ansible

• Cloud

• AWS, Azure, GCP or hybrid / multi-cloud environments

• Observability

• Prometheus, Grafana, OpenTelemetry, ELK / OpenSearch

• Security / DevSecOps

• IAM, RBAC, Vault, Secrets Management, SAST, DAST, SCA, Container Security, Policy as Code

• Data / ML / AI

• Python, MLflow, ML pipelines, Model Serving, APIs, LLMs, RAG, AI Agents

• Candidate Profile

• We are looking for a candidate with at least 5 years of professional experience, excluding internships and apprenticeships / work-study programmes.

• The candidate must also demonstrate at least 4 years of hands-on, operational and directly relevant experience on assignments related to DevOps, DevSecOps, Platform Engineering, SRE, MLOps or the industrialisation of Data & AI platforms.

• Academic experience, training projects, isolated Proofs of Concept, or experience without real production exposure will not be considered equivalent to operational experience.

A strong background in production environments is expected, with demonstrable experience in areas such as:

• availability and resilience;
• production operations and run activities;
• monitoring and observability;
• security;
• incident management;
• CI/CD and automation;
• deployment of containerised workloads;
• industrialisation of Data and AI platforms or services.

You should have solid hands-on expertise in several of the following areas:

• Kubernetes in production environments;

• CI/CD and GitOps;

• Terraform / Infrastructure as Code;

• pipeline and workload security;

• monitoring and observability;

• operation of distributed platforms;

• cloud or multi-cloud environments;

• IAM, RBAC and secrets management;

• Python / Bash scripting;

• industrialisation of Data or Machine Learning workloads.

• Hands-on experience with MLOps, LLMOps, Generative AI or AgentOps environments is a strong advantage.

• Mandatory Experience Requirements

• Minimum 5 years of professional experience, excluding internships and apprenticeships / work-study programmes.

• Minimum 4 years of concrete, operational and directly relevant experience aligned with the scope of the role.

• The candidate must be able to demonstrate real contributions to production environments, rather than purely theoretical knowledge or occasional exposure to the technologies listed above.

• Role Focus

• Primary focus: Run / Observability / Security / Industrialisation

• The role sits at the intersection of Platform Engineering, DevOps, DevSecOps, MLOps and AgentOps, with one clear objective: enabling Data & AI teams to deploy and operate use cases efficiently while ensuring security, observability, resilience, cost control and production-grade reliability.

• Here at Atos, diversity and inclusion are embedded in our DNA. Read more about our commitment to a fair work environment for all.

• Atos is a recognized leader in its industry across Environment, Social and Governance (ESG) criteria. Find out more on our CSR commitment.

• Choose your future. Choose Atos.