Contact information

You can get this candidate's contact information from https://www.work.ua/resumes/16036729/

Work experience

AI engineer

from 08.2022 to now (4 years 3 months)
Hillside IT Consulting Group, Remote (IT)

- Designed and implemented distributed AI orchestration systems managing multimodal inference (text, image, audio) at petabyte scale.
- Built large language model (LLM) fine-tuning pipelines (LoRA, QLoRA) improving domain accuracy by 42% on enterprise datasets.
- Developed vector-search infrastructure using FAISS + PostgreSQL, powering semantic retrieval for real-time recommendation APIs.
- Optimized model latency by 68% via quantization, mixed precision, and Triton inference serving.
- Automated training and deployment via Kubeflow + MLflow, reducing model-to-production time from weeks to hours.
- Created dynamic prompt-chaining framework for generative AI assistants integrated with internal APIs.
- Managed AI microservices built on FastAPI + gRPC, deployed with Kubernetes and Terraform.
- Introduced event-driven data ingestion using Kafka and Airbyte to unify logs, metrics, and telemetry pipelines.
- Built human-in-the-loop evaluation dashboards for continuous RLHF-style fine-tuning and feedback cycles.
- Partnered with the data team to standardize embeddings across vision and text models.
- Delivered AI monitoring stack with Prometheus and Grafana for real-time model drift tracking.
- Re-architected inference caching layer using Redis streams, cutting cloud inference costs by 28%.
- Integrated AWS SageMaker, ECS, and Lambda for elastic scaling of LLM workloads.
- Led a team of 6 engineers, mentoring on transformer optimization and CUDA debugging.
- Authored internal whitepaper on efficient model parallelism adopted across product teams.
- Drove AI ethics and governance policy, ensuring fairness and compliance.
- Contributed to Rust-based inference runtime to offload CPU-intensive preprocessing tasks.
- Collaborated with leadership to align AI roadmap with product KPIs and revenue targets.
- Oversaw integration of AI SDK used by partner developers across 40+ clients.
- Published open-source internal toolkit “InferKitX” for inference benchmarking.

- Tech Stack: Python, PyTorch, TensorFlow, Rust, FastAPI, gRPC, Kafka, Airbyte, Redis, PostgreSQL, FAISS, Triton, MLflow, Kubeflow, AWS SageMaker, Kubernetes, Terraform, Prometheus, Grafana

AI engineer

from 03.2020 to 08.2022 (2 years 6 months)
Beyondsoft, Remote (IT)

- Led development of production-grade deep learning APIs for recommendation and fraud detection systems.
- Built PyTorch-based training platform supporting multi-GPU distributed training on AWS EC2.
- Deployed models through Dockerized CI/CD pipelines integrating Jenkins and GitHub Actions.
- Designed ETL pipelines using Airflow, processing 10+ TB daily for analytics and model retraining.
- Engineered text-embedding service for multilingual NLP tasks using Sentence-Transformers.
- Delivered real-time anomaly detection models reducing false positives by 35%.
- Developed streaming model inference using Kafka Streams and FastAPI.
- Deployed custom transformer compression reducing memory footprint by 50%.
- Created model versioning system integrating MLflow + PostgreSQL for metadata tracking.
- Enhanced CI/CD observability with ELK stack and Prometheus monitoring.
- Partnered with data engineers on feature-store standardization using Feast.
- Implemented reinforcement learning prototype for pricing and bidding optimization.
- Migrated legacy TensorFlow 1 models to PyTorch 1.9 with 1.3× throughput gain.
- Built interactive model-explainability dashboards with SHAP and Plotly Dash.
- Mentored interns and junior ML engineers in model debugging and architecture review.
- Collaborated cross-functionally with frontend and backend teams for AI feature rollout.
- Automated A/B testing for model evaluation at scale using internal metrics framework.
- Contributed to internal AI Knowledge Base for reproducibility standards.
- Recognized with “Technical Excellence Award 2021” for innovation in ML deployment.

- Tech Stack: Python, PyTorch, TensorFlow, Airflow, MLflow, FastAPI, Kafka Streams, Jenkins, GitHub Actions, ELK Stack, Prometheus, Feast, SHAP, Plotly Dash, Docker, AWS EC2

AI engineer

from 07.2016 to 02.2020 (3 years 8 months)
CNT Management Consulting, Remote (IT)

- Built end-to-end ML workflows for image recognition and predictive analytics solutions.
- Designed RESTful AI microservices powering multiple enterprise applications.
- Developed and deployed CNN-based visual classification models with TensorFlow + Keras.
- Created custom text analytics modules (NER, sentiment, classification) for multilingual datasets.
- Automated model training pipelines via Bash and Python CLI utilities.
- Integrated ML models into .NET-based backend through gRPC and REST endpoints.
- Designed data preprocessing layer for structured/unstructured data ingestion.
- Led migration of legacy analytics scripts to unified PySpark environment.
- Worked with DevOps team to containerize AI workloads using Docker.
- Enhanced model reproducibility with versioned datasets and parameter tracking.
- Deployed models on-premise GPU clusters for local inference acceleration.
- Created performance monitoring scripts for training drift and degradation detection.
- Collaborated with business analysts to translate KPIs into measurable model metrics.
- Developed internal ML utilities library for data wrangling and preprocessing.
- Reduced training data labeling cost by 30% via semi-supervised pipeline.
- Contributed to successful delivery of 12+ AI projects across fintech and telecom domains.

- Tech Stack: Python, TensorFlow, Keras, PySpark, scikit-learn, NumPy, Pandas, gRPC, REST API, Docker, Bash, .NET, PostgreSQL, Linux, Git

Education

Kharkiv Technological University "IT STEP"

Computer Science, Lviv
Higher, from 2009 to 2014 (5 years)

Bachelor’s Degree in Computer Science

Additional education and certificates

No additional education or certificates.

Knowledge and skills

• Communicability
• JavaScript
• HTML
• CSS
• Git
• React
• Node.js
• GitHub
• TypeScript
• MySQL
• SQL
• SASS
• Figma
• PHP
• PostgreSQL
• Bootstrap
• MongoDB
• JQuery
• Redux
• Docker
• Website development on Shopify
• WordPress
• IOS development
• Android Studio
• C#
• C/C++
• Symfony
• Python
• Material UI
• Tailwind CSS
• Vue.js
• Next.js
• Express.js
• Cloudflare
• TypeORM
• Django
• AngularJS
• Docker Compose
• Azure DevOps Server
• Kubernetes
• Kotlin
• REST API
• React Redux
• React Native
• JQuery Mobile
• ASP.NET Core
• Golang
• Shopify CLI
• Blockchain
• CI/CD

Language proficiencies

• English — fluent
• Ukrainian — fluent

Additional information

Results-driven Senior AI Engineer with extensive experience designing, deploying, and optimizing large-scale AI and data systems.
Skilled in deep learning, natural language processing, computer vision, and generative model engineering, with a proven record of turning research prototypes into production-ready applications.
Expert in Python, PyTorch, TensorFlow, and modern MLOps workflows using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
Adept at building scalable model pipelines, fine-tuning LLMs, and integrating multimodal AI into enterprise environments.
Recognized for strong architectural thinking, data-driven problem solving, and cross-functional leadership that bridges research, product, and engineering teams to deliver measurable business impact.

My expected salary is $1,800–$3,500 per month, but it’s negotiable.

I can start work immediately.
Telegram: @NevaSpark