Cloud Works

Please note: This role requires working in shifts. If you're interested, I can share a draft schedule with you.

Must:
Knowledge of Kubernetes - RKE, GKE will be a plus
Hands-on experience with preparing automation scenarios using Terraform
Experience with deploying and supporting servers on cloud infrastructure, preferably Google Cloud Platform and Tencent Cloud
Knowledge of T-SQL - MS SQL, PostgreSQL will be a plus

Others:
Experience with Microsoft Windows Server
Experience with Active Directory
Expertise with preparing automation scripts to perform routine tasks using scripting languages (Bash, PowerShell, Python)
Ability to plan new infrastructure requirements and maintain current infrastructure
Understanding of network concepts - firewalls, VPN, peering (for public clouds)
English - intermediate or better

Responsibilities:
System availability and performance monitoring, on the both infrastructure and application levels.
Planned infrastructure and application updates.
New environments deployment.
Changing of the existing infrastructure according to the requirements.
Other tasks related to the system functionality

Requirements:

• 5+ experience in applying data science and machine learning techniques
• Python programming using classic scientific stack (numpy, pandas, scipy, scikit-learn, pytorch, lightgbm, matplotlib or any other visualization library)
• Understanding of classic machine learning algorithms and theory (supervised/unsupervised learning techniques, metrics and visualizations for interpretation of model performance, time-series analysis, anomaly detections, recommendation models, hyperparameter optimization)
• NLP (NLP techniques, GenAI, LLM finetuning)
• Statistics and optimization basic understanding
• Data manipulation and cleaning skills (fill missing entries / filter dataframes / apply certain changes to columns)
• Prior experience with Python web frameworks would be great, but not critical (FastAPI, Flask or anything else)
• Dockerization technologies
• Cloud solutions (any of AWS, GCP, Azure)
• Understanding of prompt engineering or experience with building scripts or applications around LLMs
• Proficient with SQL (Postgres, Snowflake), NoSQL (ElasticSearch, Mongo(optional))
• Monitoring services
   
• Responsibilities:
  Work on time series forecasting problems with real, complex data. Implement features, interpret their importance, train models and incorporate them in the production pipeline.
• Work on LLM-based solutions, which use techniques like Contextual RAG (Retrieval Augmented Generation), LLM as Judge, semantic search, chatbot services, etc.
• Create anomaly detection models and clustering methods for non-stationary, “dirty”, semi-organized data