Women Re Launch - Devops Engineer

About the Open Position

Women join us as DevOps Engineer at Dailoqa, where you will be responsible for operationalizing cutting -edge machine learning and
generative AI solutions, ensuring scalable, secure, and efficient deployment
across infrastructure. You will work closely with data scientists, ML
engineers, and business stakeholders to build and maintain robust MLOps
pipelines, enabling rapid experimentation and reliable production
implementation of AI models, including LLMs and real -time analytics systems.

To be successful as DevOps Engineer you should have
experience with:

·   Cloud sourcing, networks, VMs, performance,
scaling, availability, storage, security, access management    

·   Deep expertise in one or more cloud platforms:
AWS, Azure, GCP

·   Strong experience in containerization and
orchestration (Docker, Kubernetes, Helm)

·   Familiarity with CI/CD tools: GitHub Actions,
Jenkins, Azure DevOps, ArgoCD, etc.

·   Proficiency in scripting languages (Python,
Bash, PowerShell)

·   Knowledge of MLOps tools such as MLflow,
Kubeflow, SageMaker, Vertex AI, or Azure ML

·   Strong understanding of DevOps principles
applied to ML workflows.

Key Responsibilities may include:

·   Design and implement scalable, cost -optimized,
and secure infrastructure for AI -driven platforms.

·   Implement infrastructure as code using tools
like Terraform, ARM, or Cloud Formation.

·   Automate infrastructure provisioning, CI/CD
pipelines, and model deployment workflows.

·   Ensure version control, repeatability, and
compliance across all infrastructure components.

·   Set up monitoring, logging, and alerting
frameworks using tools like Prometheus, Grafana, ELK, or Azure Monitor.

·   Optimize performance and resource utilization of
AI workloads including GPU -based training/inference

·   Experience with Snowflake, Databricks for
collaborative ML development and scalable data processing.

·   Understanding model interpretability,
responsible AI, and governance.

·   Contributions to open -source MLOps tools or
communities.

·   Strong leadership, communication, and
cross -functional collaboration skills.

·   Knowledge of data privacy, model governance, and
regulatory compliance in AI systems.

·   Exposure to LangChain, Vector DBs (e. g. ,
FAISS, Pinecone), and retrieval -augmented generation (RAG) pipelines.