MLOps Engineer

7 hours ago

ClujNapoca, Cluj, Romania RebelDot Full time 60,000 - 120,000 per year

You might be our missing piece if you have:

  • Strong expertise in Python and AI frameworks such as PyTorch, Keras, SciPy, or Tensorflow.
  • Experience with Python-based Web frameworks like FastAPI, Flask, or Django.
  • Knowledge of PEP 8 coding standards for Python.
  • Extensive experience in solving AI/ML challenges and working with LLMs.
  • Familiarity with OpenAI, Embeddings, Completion, and Semantic Search.
  • Solid experience with API integrations and working with external APIs like OpenAI, Anthropic, or similar AI service providers.
  • Hands-on experience with containerization and orchestration tools – especially Docker for packaging ML models, and Kubernetes (or similar) for deploying and scaling them in distributed environments.
  • Proficiency in DevOps and automation practices: designing CI/CD pipelines (using tools like Jenkins, GitLab CI/CD, or GitHub Actions) to automate model testing and deployment, and using Infrastructure-as-Code (CloudFormation, Terraform) to manage cloud resources.
  • Working knowledge of cloud computing services (AWS, Azure, GCP) for ML workloads. This includes familiarity with cloud AI/ML services and managed ML platforms (like SageMaker, Azure ML, or GCP AI Platform) and experience setting up scalable infrastructure for data and models (compute instances, storage, networking for model endpoints).
  • Familiarity with databases and experience using SQLAlchemy, Alembic, and database management for AI models.
  • Strong skills in managing datasets using tools like Pandas, SciPy, and Numpy for data pre/post-processing.
  • Experience with monitoring and logging frameworks to track running systems; Prometheus/Grafana or cloud monitoring services to record model serving performance metrics, and possibly specialized ML monitoring solutions (e.g. MLflow, Weights & Biases, Apache Airflow for scheduling retraining).
  • Strong analytical and problem-solving skills to diagnose issues from logs/metrics and tune system performance.
  • Excellent communication skills and a collaborative mindset; Since this role works across AI Engineering, Data Engineering, DevOps Engineering, and client teams, the engineer must be able to explain technical concepts to diverse stakeholders and document work clearly.
  • Ability to work in an agile environment, manage priorities, and coordinate with remote or cross-functional team members is important.

We would be thrilled if you have:

  • A track record of deploying and managing machine learning models at scale (e.g., in a product or platform used by thousands of end-users or clients).
  • Experience working on client-facing projects or consulting engagements.

We will be working together on:

  • Designing, building, and automating ML pipelines.
  • Deploying and scaling models in production.
  • Monitoring, maintaining, and improving model performance.
  • Collaborating with Data Engineers and client stakeholders.
  • Establishing governance, documentation, and best practices.

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