Description

I’m a Senior Software Engineer with 12+ years of experience designing and delivering scalable software solutions across AI, data engineering, and full-stack development. I specialize in building reliable, high-performance systems that power modern applications and real-time data platforms. My background at Accenture strengthened my ability to work in complex enterprise environments, architect robust solutions, and collaborate with global teams.

My core technical expertise includes Python (FastAPI, Django, Flask), Node.js (Express, NestJS), Java, Golang, TypeScript, React, Angular, and Next.js. I have extensive experience with databases such as PostgreSQL, MySQL, MongoDB, and ClickHouse, and I build data pipelines using Spark, PySpark, Databricks, and Delta Lake. I work on AI/ML solutions with LLMs, LangChain, LlamaIndex, Hugging Face, and MLflow, focusing on retrieval-augmented generation (RAG) and intelligent automation.

On the cloud and DevOps side, I’m skilled in AWS, GCP, Azure, Docker, Kubernetes, Terraform, and CI/CD pipelines. I emphasize clean architecture, performance optimization, testing, and observability to ensure reliability at scale.

I thrive in remote, cross-functional teams and enjoy solving complex technical problems that deliver real business impact. I’m open to senior engineering roles across EU/UK, where I can contribute my experience in AI, data platforms, and modern software engineering.

Languages

English
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Accenture
Senior AI / Machine Learning Engineer
September 2023 - October 2025 (2 years and 1 month)
Poland
• Designed and deployed end-to-end ML pipelines on AWS SageMaker and Azure ML for predictive analytics and generative AI solutions.
• Implemented NLP models (BERT, GPT-based transformers) for automated document classification and summarization.
• Built and optimized deep learning architectures in TensorFlow and PyTorch, improving model accuracy by 27%.
• Developed MLOps pipelines using MLflow and Kubeflow for versioned model tracking and CI/CD integration.
• Deployed real-time inference APIs using FastAPI and Dockerized microservices.
• Automated data ingestion and preprocessing using Python (Pandas, NumPy, Scikit-learn), reducing ETL latency by 35%.
• Leveraged GCP Vertex AI for scalable model deployment and monitoring with Prometheus & Grafana dashboards.
• Collaborated with cross-functional stakeholders to translate business objectives into production ML deliverables.
• Ensured compliance and data governance via reproducible training workflows.
• Mentored junior engineers in MLOps best practices and cloud AI deployment.
Python ETL (Extract, Transform, Load) Processes CI/CD Management React.js Pandas
Britenet
Machine Learning Engineer | AI Systems Developer
February 2021 - June 2023 (2 years and 4 months)
Warsaw, Poland
• Developed and deployed computer vision models (CNNs with PyTorch) for defect detection and product quality assessment.
• Integrated AWS SageMaker pipelines with Docker and GitLab CI/CD, achieving 45% faster model iteration.
• Engineered automated hyperparameter tuning workflows using Ray Tune and Optuna.
• Enhanced data annotation workflows through custom labeling tools and Active Learning strategies.
• Built real-time inference services and optimized serving latency via TensorRT.
• Deployed time-series forecasting models to predict key business KPIs with 90% accuracy.
• Collaborated with BI teams to connect ML outputs with Power BI and Tableau dashboards.
• Implemented model monitoring with drift detection and retraining triggers.
• Conducted A/B testing and model explainability analysis using SHAP and LIME.
• Promoted Agile and DevOps culture within the AI team, improving sprint delivery consistency.
Perficient
Data & AI Engineer
June 2018 - November 2020 (2 years and 5 months)
Warsaw, Poland
• Designed and implemented data processing pipelines using Python, Airflow, and Spark for ML readiness.
• Trained classification and regression models for customer segmentation and churn prediction.
• Deployed scalable ML workloads via Dockerized microservices integrated with Azure ML.
• Built custom APIs for model inference in Flask and FastAPI serving 100K+ monthly predictions.
• Leveraged TensorFlow Extended (TFX) for automated training and validation workflows.
• Integrated cloud storage solutions (S3, Azure Blob, GCS) for distributed data management.
• Applied feature engineering and dimensionality reduction (PCA, t-SNE) to improve training efficiency.
• Established ML lifecycle governance through experiment tracking and reproducible datasets.
• Enhanced CI/CD pipelines with automated unit and integration tests for ML components.
• Collaborated in Agile sprints to deliver business-aligned ML features on time.