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Nik Pocuca

@nikpocuca

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Senior AI software engineer specializing in predictive and generative models.

Cyprus

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What I'm looking for

I seek a role that fosters innovation and collaboration, with opportunities for growth.

I am a senior AI software engineer with over eight years of experience specializing in predictive and generative models. My expertise lies in providing complete end-to-end solutions for financial and AI-driven industries. I hold a doctorate in mathematics with a focus on computational statistics from McMaster University, where I also served as a Lecturer, teaching master’s level courses.

Throughout my career, I have worked with notable clients such as BBVA, Société Générale, Goldman Sachs, and HSBC, delivering AI models that drive significant business value. My most recent role as CEO and Lead Quantitative Engineer at Aegios Solutions Inc. has allowed me to lead AI development for financial workflows, achieving impressive results including a 1.3 million ARR through innovative cloud solutions on platforms like AWS and Azure.

In addition to my technical skills, I am passionate about teaching and mentoring the next generation of data scientists. My experience as a Sessional Faculty at the University of Toronto has equipped me with the ability to communicate complex concepts effectively, ensuring that my students are well-prepared for their careers in data science.

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Experience

Work history, roles, and key accomplishments

Current

Quantitative Engineer – CEO

Current

Aegios Solutions Inc.

Dec 2023 - Present (1 year 11 months)

Led quantitative engineering specializing in AI development for financial workflows, quantitative modeling, and option pricing. Provided development services for international technology companies and built cloud solutions on AWS, Azure, Google Cloud Platform, and OpenAI platforms, achieving 1.3 million ARR.

AI Development Options Pricing AWS Azure Google Cloud Platform Workflows

Senior ML Engineer – Inference

Tenstorrent

Mar 2024 - Present (1 year 8 months)

Developed high-performance inference software for LLMs and stable-diffusion models, achieving 103% faster inference times. Programmed in Python, C++, CUDA, and various TensorRT frameworks, and performed comprehensive benchmarking across hardware configurations. Created Apache Superset pipelines and dashboards for customer success teams.

LLMs Stable Diffusion Python

Counterparty Credit Risk – Senior Business Analyst

Bank of Montreal

Jan 2024 - Present (1 year 10 months)

Developed a data rendering engine to integrate a third-party vendor system for mark-to-market stress tests, ensuring compliance with OSFI regulators. Programmed in Python for converting XML files and querying Postgress SQL, Oracle SQL, and SQL Server. Performed data analysis and quality checks, delivering a 6-month regulatory project in 3 months.

Python PostgreSQL Oracle

Sessional Faculty

University of Toronto

Sep 2022 - Present (3 years 2 months)

Served as Head Instructor for the master’s level course STA2453H1Y - Data Science Methods, Collaboration, and Communication. Delivered course content related to data collection, wrangling, Python programming, data exploration, visualization, modeling, and reproducibility.

Data Science Python Data Wrangling

AI Research Engineer

Riskfuel

May 2021 - Present (4 years 6 months)

Developed production-level AI solutions for financial and risk models using PyTorch and C++. Containerized and created internal APIs with Kubernetes and Docker for scalable data generation strategies and analytical tools. Delivered financial and predictive models to major clients including BBVA, HSBC, and Goldman Sachs.

Financial Modeling Risk Modeling PyTorch

Software Developer – Machine Learning

Population Health Research Institute

Jan 2018 - Present (7 years 10 months)

Programmed in Python using Keras and Tensorflow for CNNs in scoring MoCA clock/cube drawings. Implemented image processing and edge detection in MATLAB for image data capture and formatting. Contributed to research on CNN implementation for early detection of mental ailments and won the 2018 Johns Hopkins MedHacks Winner Adherence Track.

Python Keras TensorFlow