DevOps Engineer

4 weeks ago

New Delhi, India Tipstat® Full time

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.

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