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CV Overview
- Experience: 5+ years
- Location: Eastern Europe (remote-ready)
- Level of English: Upper-Intermediate
- Available for: Dedicated team & FLEX (on-demand) cooperation
Here are the key highlights of Andriy’s resume — a talented AI DevOps engineer ready to make your ML systems production-ready, adaptable, and maintainable.
Key Competencies of the AI Infrastructure Engineer
AI and ML
– Manages infrastructure to support training and deployment of ML models
– Implements pipelines for supervised and unsupervised learning workflows
– Monitors performance of deployed models and supports retraining strategies
– Collaborates with ML engineers to operationalize models like CNNs, RNNs, and transformers
Development Toolkit
– Python, Bash, SQL
– TensorFlow, PyTorch, Scikit-learn, Keras
– Pandas, NumPy, OpenCV
– MLflow, DVC, Optuna, Weights & Biases
– Git, Docker, Kubernetes
– AWS, GCP, Azure
Deployment
– Deploys machine learning models using containerized environments
– Uses Docker and Kubernetes to coordinate services and manage workloads
– Works across cloud platforms like AWS, GCP, and Azure
– Builds clean, reliable APIs and integrates models into CI/CD pipelines
Soft Skills
– Applies systems thinking and a solutions-oriented mindset
– Collaborates seamlessly with data scientists, ML engineers, and product teams
– Documents workflows for knowledge sharing and reproducibility
– Engages actively in sprint planning, reviews, and retrospectives
How This Engineer Can Help Your Business
Deploys Models Into Production
Containerizes models using Docker, deploys them via Kubernetes or serverless APIs, and ensures their fast, secure integration with your systems.
Optimizes Cloud Infrastructure
Designs cost-efficient, GPU-ready cloud environments using AWS, GCP, or Azure. Ensures their compliance, scalability, and high availability.
Bridges DevOps and Data Science
Transforms ML experiments into production-ready systems. Ensures code reproducibility, clean handoffs, and collaborative delivery.
Builds Automated ML Pipelines
Speeds up your ML development with automated workflows. Creates CI/CD pipelines that handle training, testing, deployment, and monitoring.
Monitor and Maintain Model Health
Keeps your models updated by tracking performance in real time—accuracy, drift, latency, and more. Spots issues early and retrains models when needed.
Ensure Long-Term AI Reliability
Builds infrastructure that works effectively and grows with your business. Improves your AI system’s visibility and minimizes downtime, supporting your long-term success.
Previous Projects of This AI Systems Engineer
1
MLOps Pipeline for Insurance Models
Designed a CI/CD pipeline to automate model retraining and deployment. Reduced release time from days to under 2 hours.
2
Cloud-Native Infrastructure for E-Commerce AI
Set up scalable Kubernetes clusters with GPU autoscaling on GCP. Improved system uptime by 40% during peak load.
3
Model Monitoring for Healthcare Predictions
Integrated Prometheus and Grafana to track model drift and latency. Enabled early detection of performance drops in production models.
How to Hire This AI Infrastructure Engineer
Tech Stack Summary
Languages:
- Python
- Bash
- YAML
- SQL
MLOps & Workflow Tools:
- MLflow
- Kubeflow
- Airflow
- DVC
Containerization & Orchestration:
- Docker
- Kubernetes
- Helm
Cloud Platforms:
- AWS (EC2, SageMaker, EKS)
- GCP (Vertex AI, GKE)
- Azure ML
CI/CD & Automation:
- GitHub Actions
- Jenkins
- GitLab CI/CD
Monitoring & Logging:
- Prometheus
- Grafana
- ELK Stack
- Sentry
Version Control & Repos:
- Git
- GitHub
- Bitbucket
Infrastructure-as-Code:
- Terraform
- CloudFormation
- Ansible
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industry Expertise:
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Vasyl K.
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industry Expertise:
- E-Commerce
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Tom R.
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Technology Stack:
- Python
- GitLab CI/CD
- Jenkins
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industry Expertise:
- Retail
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Ivana D.
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industry Expertise:
- Telecom
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About Mobilunity
30-50% lower
Costs compared to in-house hiring
3-6 weeks
To find and onboard your best-fit experts
500+ candidates
Vetted by proficient tech recruiters
5+ years
Average experience of candidates
40+
Active customers in 15 countries
4.2 years
Average client partnership duration
Why Hire AI DevOps Engineer With Mobilunity
Cut Costs up to 2X
We can help you hire remote experts from top offshore and nearshore locations. Due to lower living costs in those regions, you can save up to 50% of development costs.
Hire in 3-6 Weeks
You get the first CVs during the first week of our cooperation. So, we can vet, interview, and onboard your selected candidates within 3-6 weeks.
Get 360° Admin Support
You focus on your project while Mobilunity takes the administrative burden off you. We cover accounting, legal, HR, and IT helpdesk assistance for your team.
Leverage 93% Retention Rate
93% of our candidates successfully pass probation and remain with a client for an average of 3 years, ensuring consistent delivery and long-term ROI.
Get Long-Term Commitment
The average duration of our collaboration with clients is 4.2 years. We focus on strategic relationships and your success, building deep, meaningful partnerships.
Scale up or Down Based on Your Needs
You can start by engaging a single AI DevOps expert for just a few hours a month, then scale up to a team of 10+ full-time tech professionals.
FAQ on Engaging AI DevOps Engineers With Mobilunity
Our Expert Insights
Client Testimonial
Mobilunity provides an excellent and efficient tech talent service. All my staffing requests, even for niche technologies, were fulfilled in a 2 to 4-week time period, with top-notch candidates. Besides that, I like the caring environment that the Mobilunity team provides for the developers and ensures that everybody is able to work without distractions.
I strongly recommend Mobilunity as a tech talent partner!
Waldemar Biller
Head of Technology
Global Tech Talent solutions from Mobilunity:
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