Quantori

- Led 8-person cross-functional team in developing RAG-powered candidate matching system (Azure AI infrastructure)
- Architected document processing pipeline handling 500+ CVs/day using LangChain orchestration and OpenAI embeddings
- Mentored 3 developers in RAG implementation best practices while coordinating with QA lead to build AI testing framework from scratch
- Team Lead responsibilities: Technical roadmap planning, sprint task allocation, and AI ethics compliance verification
- Engineered hybrid ranking system combining GPT-4 embeddings with semantic reranking models (faithfulness to Responsible AI principles)

Key Technologies: Azure AI Services, OpenAI GPT-4, LangChain, Python, RAG Architecture, CI/CD Pipelines, Responsible AI

Senior Software Engineer

# Building a pipeline file processing tool aims to automate generation of picklist instructions for the Acoustic Liquid Handlers

- Contributed to building a pipeline file processing tool to automate picklist instructions generation for the Acoustic Liquid Handlers.
- Supported and developed complex Python transforms on the Palantir Foundry platform, enhancing data analysis capabilities and increasing efficiency.
- Increased test coverage from 70% to 95%, ensuring improved quality and stability of pipeline systems.
- Doubled the number of pipeline modes, resulting in increased speed of customer's experimental work.
- Communicated effectively with customers to clarify business requirements, enhancing customer satisfaction and retention.
- Provided continuous technical support for a customer-critical application, improving customer satisfaction and loyalty.

Environment: Palantir Foundry, Python, Spark, pandas, pytest, openpyxl



# Developed the backend of a Laboratory Information Management System.

-I mplemented robust endpoints utilizing FastAPI and GraphQL, resulting in improved API performance and efficiency.
- Built powerful data models using strawberry, increasing scalability and flexibility of data storage.
- Streamlined container building instructions, improving deployment speed and efficiency.

Environment: Python, FastAPI, GraphQL, Docker, Bitbucket, strawberry, pytest



# Creating scripts for parsing files on the Dotmatics platform

- Successfully developed and implemented scripts for parsing various file formats on the Dotmatics platform, receiving positive feedback on data processing accuracy and efficiency.
- Tested, debugged, and integrated the scripts into the project workflow, resulting in a streamlined and automated data processing pipeline.
- Continuously updated and improved the scripts, providing valuable technical expertise and delivering high-quality code on time.

Environment: Dotmatics, Python2.5, git

Software Engineer

# Discovering concealed connections among illnesses through the examination of EHR with the aid of machine learning techniques

- Discovered concealed connections among illnesses through examination of Electronic Health Records (EHR) using machine learning techniques.
- Developed a comprehensive data collection and preparation pipeline, improving efficiency and accuracy of data analysis.
- Conducted cutting-edge data science experiments, analyzing and reporting results to drive business decisions and improve performance.
- Implemented a new data visualization pipeline to evaluate model performance and identify areas for improvement.
- Designed and developed unit tests to ensure reliability and accuracy of data analysis processes.

Environment: Python, AWS, JupyterHub, GitLab, Docker, sqlalchemy, pandas, statsmodels, gensim



# Building a pipeline document parsing tool and a full-text search engine for biopharma companies

- Built a pipeline document parsing tool and a full-text search engine for biopharma companies.
- Developed, tested, and debugged software tools to facilitate user experience.
- Improved the scientific NLP engine.
- Created a text scraping and converting tool for extracting data from files of different formats and converting it to .xml format.
- Created an ontology for the NLP engine.

Environment: Python, PostgreSQL, AWS, GitLab, Bitbucket, pytest



# Developing a desktop GUI application for spectra image scientific preprocessing

- Developed a desktop GUI application for spectra image scientific preprocessing.
- Developed the GUI with Qt Designer and the application's back-end components.
- Designed and created test cases and performed manual testing.
- Created an automated scientific image analysis tool.

Environment: Python, PySide2, Qt Designer, NumPy, Matplotlib

Junior Software Engineer

Ioffe Institute

Scientific research in experimental and theoretical physics. Delivering data collection, processing, analysis, and visualization automation tools for a physics and technology research institute

Tasks and Achievements: 
Enabled automated experiment data collection
Developed a data processing tool with Python
Performed and conducted a multivariate statistical analysis of experimental data 
Developed an automated reporting tool
Authored multiple publications and conference proceeding

Scientific Researcher

Experienced Senior Software Engineer and AI Team Lead with a proven track record of designing and deploying enterprise-scale AI solutions. I specialize in bridging complex technical challenges with business outcomes, LLMs and RAG to solve real-world problems.

With a background in physics and scientific research, I bring a data-driven, analytical mindset to software development. Over the past 3+ years, I’ve led teams to build innovative tools for biotech, healthcare, and lab automation, including AI-driven candidate matching systems that improve hiring efficiency.

Taplio

Nikolay Bezverkhnii's Linkedin Analytics

Want to drive more opportunities from LinkedIn?

Taplio

.css-1jgreyo{background-image:linear-gradient(284deg, rgba(39,43,72,1) 0%, rgba(33,47,183,1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Nikolay Bezverkhnii's Linkedin Analytics

Want to drive more opportunities from LinkedIn?

Nikolay Bezverkhnii's Linkedin Analytics