MLOps Engineer

Stack

Responsibilities

• ▹Design, implement, and operate scalable cloud infrastructure on AWS, GCP, or Azure for data and ML workloads
• ▹Automate infrastructure and deployments using Terraform, Ansible, Helm, and Kubernetes
• ▹Build and maintain CI/CD pipelines for continuous deployment of ML models, AI agents, and observability platforms
• ▹Manage containers and orchestration with Docker and Kubernetes (EKS)
• ▹Build and maintain reproducible training, validation, and inference pipelines (Airflow, MLflow, DVC, Spark)
• ▹Implement and optimize monitoring, logging, and observability solutions (Prometheus, Grafana, ELK/EFK, OpenTelemetry)
• ▹Ensure the security, availability, and resilience of cloud platforms
• ▹Integrate the full Data -> ML -> Deployment -> Monitoring cycle, working closely with Data, ML, DevOps, and Backend teams
• ▹Collaborate with ML teams to package and deploy models and AI agents to production
• ▹Participate in designing hybrid pipelines combining traditional ML, generative AI, and LLM-based agents (LangChain, LangGraph, CrewAI)

Requirements

• ▹Degree in Computer Engineering, Software, Telecommunications, or a related field
• ▹3+ years of experience in automation, deployment, or cloud infrastructure management
• ▹Solid experience with infrastructure as code and automation (Terraform, Helm, Ansible)
• ▹Advanced knowledge of Linux, networking, and distributed systems
• ▹Hands-on experience with CI/CD pipelines (GitLab CI, Jenkins, ArgoCD, FluxCD)
• ▹Proficiency with Docker and Kubernetes
• ▹Experience with model and data management tools such as MLflow, DVC, or Vertex AI
• ▹Knowledge of monitoring, logging, and observability
• ▹Experience with SQL and NoSQL databases (PostgreSQL, TimescaleDB, MongoDB)
• ▹Ability to diagnose and resolve incidents in high-traffic production environments, optimizing cost and performance

Nice to have

• ▹Experience with messaging and streaming systems such as Kafka, Redpanda, or Benthos
• ▹Knowledge of serverless and event-driven architectures (AWS Lambda, SNS/SQS)
• ▹Experience with advanced observability and model performance metrics
• ▹Knowledge of Site Reliability Engineering (SRE) practices
• ▹Experience with modern MLOps platforms (Kubeflow, MLflow)
• ▹Familiarity with automated deployments and DevOps best practices
• ▹Knowledge of generative AI infrastructure (GPU, optimized containers, RAG serving)
• ▹Advanced technical English, written and spoken

Soft skills