DevOps Engineer

We are looking for a highly skilled DevOps Engineer with strong experience in DevSecOps and MLOps / LLMOps to design, automate, and secure our development and deployment pipelines.You will play a critical role in building scalable, secure, and production-ready infrastructure to support both traditional applications and machine learning / LLM workloads.This role demands a strong understanding of Kubernetes, CI/CD pipelines, infrastructure-as-code, model lifecycle management, and cloud-native security practices.

DevOps & Infrastructure

• Design, implement, and manage scalable, fault-tolerant infrastructure on cloud or hybrid environments (AWS / GCP / Azure / Hetzner / Bare metal).
• Develop and maintain CI/CD pipelines using tools like GitHub Actions, GitLab CI, Jenkins, or ArgoCD.
• Manage containerized workloads using Kubernetes, Helm, and Docker.
• Implement infrastructure as code (IaC) with Terraform / OpenTofu / Terragrunt.
• Monitor system performance, availability, and cost efficiency using Prometheus, Grafana, ELK, or Loki.

DevSecOps

• Integrate security automation into CI/CD pipelines (SAST, DAST, SCA, dependency scanning).
• Implement policy as code using OPA / Conftest and enforce RBAC / IAM best practices.
• Manage secrets and credentials using tools like Vault, Sealed Secrets, or External Secrets Operator.
• Set up vulnerability scanning and runtime protection (e.g., Trivy, Falco, Aqua Security).
• Define security baselines for infrastructure, network, and containers.

MLOps / LLMOps

• Collaborate with ML and data teams to operationalize model training, evaluation, and deployment.
• Build automated pipelines for data preprocessing, model training, and inference deployment using tools like Kubeflow, MLflow, or Airflow.
• Manage feature stores, model registries, and monitoring for drift, latency, and accuracy.
• Support LLM pipelines — prompt orchestration, fine-tuning, vector DB integrations, and retrieval-augmented generation (RAG).
• Optimize GPU-based workloads and manage distributed training / inference infrastructure.

Required Skills & Qualifications

• Languages: Python, Bash, Go (preferred)
• IaC Tools: Terraform / OpenTofu / Terragrunt
• CI/CD: GitHub Actions, GitLab CI, Jenkins, ArgoCD
• Containers: Docker, Kubernetes, Helm
• Monitoring: Prometheus, Grafana, Loki, ELK
• Security: Trivy, Falco, Vault, OPA, Snyk
• MLOps Tools: MLflow, Kubeflow, Airflow, Weights & Biases
• Cloud Platforms: AWS / GCP / Azure / Hetzner
• Databases: PostgreSQL, Redis, Vector DBs (Milvus, Pinecone, Weaviate, Qdrant)

Nice to Have

• Experience with GPU orchestration on Kubernetes (NVIDIA operator, KServe).
• Exposure to LLM frameworks (LangChain, LlamaIndex, vLLM, Ollama).
• Knowledge of data governance and compliance (GDPR, SOC2).
• Experience with self-hosted runners, GitOps, or multi-cluster management.
• Familiarity with event-driven systems (Kafka, NATS, or Redis Streams).

What We Offer

• Opportunity to work on challenging, large-scale systems with real-world impact.
• Collaborative team culture with focus on learning and innovation.
• Competitive compensation and growth opportunities.