DevOps Engineer

About the Open Position<\/b>
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Women<\/b> join us as DevOps Engineer<\/b> at Dailoqa, <\/b>where you will be responsible for operationalizing cutting\-edge machine learning and\ngenerative AI solutions, ensuring scalable, secure, and efficient deployment\nacross infrastructure. You will work closely with data scientists, ML\nengineers, and business stakeholders to build and maintain robust MLOps\npipelines, enabling rapid experimentation and reliable production\nimplementation of AI models, including LLMs and real\-time analytics systems.
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To be successful as DevOps Engineer you should have\nexperience with:<\/b>
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· <\/span><\/span><\/span><\/span>Cloud sourcing, networks, VMs, performance,\nscaling, availability, storage, security, access management <\/span>
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· <\/span><\/span><\/span><\/span>Deep expertise in one or more cloud platforms:\nAWS, Azure, GCP
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· <\/span><\/span><\/span><\/span>Strong experience in containerization and\norchestration (Docker, Kubernetes, Helm)
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· <\/span><\/span><\/span><\/span>Familiarity with CI/CD tools: GitHub Actions,\nJenkins, Azure DevOps, ArgoCD, etc.
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· <\/span><\/span><\/span><\/span>Proficiency in scripting languages (Python,\nBash, PowerShell)
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· <\/span><\/span><\/span><\/span>Knowledge of MLOps tools such as MLflow,\nKubeflow, SageMaker, Vertex AI, or Azure ML
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· <\/span><\/span><\/span><\/span>Strong understanding of DevOps principles\napplied to ML workflows.
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Key Responsibilities may include:<\/b>
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· <\/span><\/span><\/span><\/span>Design and implement scalable, cost\-optimized,\nand secure infrastructure for AI\-driven platforms.
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· <\/span><\/span><\/span><\/span>Implement infrastructure as code using tools\nlike Terraform, ARM, or Cloud Formation.
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· <\/span><\/span><\/span><\/span>Automate infrastructure provisioning, CI/CD\npipelines, and model deployment workflows.
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· <\/span><\/span><\/span><\/span>Ensure version control, repeatability, and\ncompliance across all infrastructure components.
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· <\/span><\/span><\/span><\/span>Set up monitoring, logging, and alerting\nframeworks using tools like Prometheus, Grafana, ELK, or Azure Monitor.
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· <\/span><\/span><\/span><\/span>Optimize performance and resource utilization of\nAI workloads including GPU\-based training/inference
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· <\/span><\/span><\/span><\/span>Experience with Snowflake, Databricks for\ncollaborative ML development and scalable data processing.
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· <\/span><\/span><\/span><\/span>Understanding model interpretability,\nresponsible AI, and governance.
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· <\/span><\/span><\/span><\/span>Contributions to open\-source MLOps tools or\ncommunities.
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· <\/span><\/span><\/span><\/span>Strong leadership, communication, and\ncross\-functional collaboration skills.
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· <\/span><\/span><\/span><\/span>Knowledge of data privacy, model governance, and\nregulatory compliance in AI systems.
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· <\/span><\/span><\/span><\/span>Exposure to LangChain, Vector DBs (e. g. ,\nFAISS, Pinecone), and retrieval\-augmented generation (RAG) pipelines.
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