Spotware Systems

Led and mentored a team of 20 junior developers and analysts, fostering expertise in data engineering, trading analytics, generative AI, and capital markets with a strong emphasis on real-world FOREX trading systems and scalable data platforms.

• Designed and launched an Analytics department from the ground up, aligning technical, financial, and business intelligence prioritiesv-powered by LLMs (e.g., GPT-4o, Claude) for automated trading insights and executive reporting.
• Built a scalable data infrastructure using hybrid cloud architecture (AWS Redshift + on-prem data lake), optimized for cost and performance through query performance tuning in Redshift and integration of SQL-based pipelines for high-frequency FX data ingestion and AI inference.
• Applied data modeling principles and Kimball methodologies to architect analytics layers supporting multi-asset trading strategies and financial forecasting.
• Implemented robust ETL/ELT pipelines with Airflow, Spark, and DBT (Data Build Tool) to ensure data integrity, modularity, and pipeline observability across structured trade data and real-time FX streams.
• Designed modular data services and APIs including RESTful endpoints to support operational integration with market data feeds, trading platforms, and real-time signal generation for automated FX bots.
• Built analytics and reporting capabilities tailored to FOREX and multi-asset strategies, integrating TA-Lib, pandas-ta, and Redshift Console for technical indicator computation and interactive analytics access.
• Developed CI/CD workflows using Docker and GitLab CI, accelerating deployment of dashboards, AI/ML models, and Redshift updates across environments with embedded monitoring via CloudWatch.
• Standardized KPI metrics and dashboard definitions across trading desks, enabling consistent performance tracking; introduced LLM-based natural language query layers on top of Redshift and SQL views for accessible, ad-hoc querying by non-technical stakeholders.

Head of Business Intelligence  / Data & Product (FX Trading)

cryptoinsights

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Civitta

Led 3 McKinsey projects in collaboration with architects, planners, and project managers to embed advanced modeling into 3D displays and simulationsenhanced by generative AI models for automated design suggestions, semantic annotation generation, and simulation-driven narratives.

• Worked with large-scale datasets, integrating SQL and Amazon Redshift for data warehousing, efficient querying, and query performance tuning in Redshift to support responsive, accurate 3D visualizations of Expo sites and infrastructure.
• Integrated text-to-3D AI tools and synthetic data generation pipelines to accelerate prototyping, spatial storytelling, and design iteration.
• Applied data modeling principles and Kimball methodology to architect schema for 3D spatial datasets; leveraged DBT (Data Build Tool) for maintaining modular, testable data transformations across the pipeline.
• Implemented spatial data processing best practices to optimize performance and fidelity, while leveraging CloudWatch and Redshift Console for monitoring, logging, and real-time system diagnostics.
• Continuously refined and optimized workflows for scalability and real-time responsiveness, incorporating LLMs to automate documentation, template generation, and DevOps support functions.
• Designed and deployed Python-based back-end systems, embedding LLM copilots for prompt-based code generation, automated test creation, and intelligent tooling support in engineering operations.
• Managed end-to-end data pipelines for the automated generation and semantic tagging of 3D models aligned with project goals and geospatial metadata standards.

Senior Data Scientist / Senior Software Engineer (Data, AI, Python Apps) 

Wireless Media

Led and mentored junior engineers to enhance efficiency and reduce costs in the remittance process for a financial institution while building cloud-scale data pipelines leveraging best practices in data modeling principles (including Kimball) and distributed systems.

• Achieved a $700k reduction in transaction costs and 26% faster transfer times by implementing Apache Spark for large-scale data processing and optimizing pipeline performance.
• Developed fraud detection models that increased fraud detection accuracy by 35%, using advanced feature engineering and scalable compute.
• Utilized Hyperledger Fabric for blockchain-based smart contracts to ensure secure, transparent transactions.
• Designed and developed distributed, cloud-scale data pipelines in Azure, incorporating SQL, DBT (Data Build Tool), and Amazon Redshift to optimize performance and maintain data integrity.
• Applied query performance tuning in Redshift and managed data access and quality using Redshift Console and CloudWatch, resulting in better pipeline reliability and observability.
• Enhanced Azure's Compute Capacity Central (CCC) and Compute Management and Automation System (CMAS) by engineering core services that tracked and managed Azure’s compute capacity usage, maintained buffer thresholds, and delivered telemetry—boosting capacity management efficiency by 30%.
• Transformed and standardized raw data into analytics-ready formats for downstream use and tool development, streamlining processing workflows and data availability.

Senior Data Engineer

Telesign

Led and mentored junior engineers to enhance efficiency and reduce costs in the remittance process for a financial institution while building cloud-scale data pipelines leveraging best practices in data modeling principles (including Kimball) and distributed systems.

• Achieved a $700k reduction in transaction costs and 26% faster transfer times by implementing Apache Spark for large-scale data processing and optimizing pipeline performance.
• Developed fraud detection models that increased fraud detection accuracy by 35%, using advanced feature engineering and scalable compute.
• Hyperledger Fabric for blockchain-based smart contracts to ensure secure, transparent transactions.
• Designed and developed distributed, cloud-scale data pipelines in Azure, incorporating SQL, DBT (Data Build Tool), and Amazon Redshift to optimize performance and maintain data integrity.
• Applied query performance tuning in Redshift and managed data access and quality using Redshift Console and CloudWatch, resulting in better pipeline reliability and observability.
• Enhanced Azure's Compute Capacity Central (CCC) and Compute Management and Automation System (CMAS) by engineering core services that tracked and managed Azure’s compute capacity usage, maintained buffer thresholds, and delivered telemetry-boosting capacity management efficiency by 30%.
Transformed and standardized raw data into analytics-ready formats for downstream use and tool development, streamlining processing workflows and data availability.

AI Technical Product Manager

BlackRock

Worked on a project for the world’s largest asset manager (US$9.4 trillion), where I led the development of a machine learning model from scratch to auto-label large volumes of unstructured PDF documents related to Private Equity and Alternative Investments.

• Led the end-to-end development of a machine learning model to auto-label unstructured PDF documents related to Private Equity and Alternative Investments.
• Acted as the primary liaison for over 50 General Partners (GPs), validating fund-submitted data and ensuring adherence to stringent data quality standards.
• Built and deployed a custom ML pipeline on AzureML, integrating SQL-based preprocessing and DBT models to transform fund documents into structured, labeled datasets for downstream analytics.
• Improved data reliability by 25% and eliminated manual tagging bottlenecks by automating classification and entity extraction from complex, non-standard financial documents.
• Applied strong data engineering and validation practices, leading to a 40% quarter-over-quarter reduction in inconsistencies and increasing trust across internal and external stakeholders.
• Embedded multi-level data quality controls in the pipeline, enhancing precision by 20% through iterative QA and anomaly correction.
• Leveraged LLM-powered validation tools and process automation to reduce quarterly reporting turnaround time by 15%.
• Stored clean, structured outputs in Amazon Redshift, applying query performance tuning for fast retrieval and integrating Redshift Console and CloudWatch for monitoring, alerting, and system diagnostics.
• Modeled datasets using Kimball-style dimensional design, supporting auditability and repeatable analytics workflows.

Investment Data Engineer

Worked with a diversified client structure on various portfolios.

• Analyzed complex financial datasets to understand client-specific needs and challenges, applying statistical modeling and machine learning techniques to develop data-driven solutions aimed at increasing profitability and minimizing risk exposure.

• Built and deployed financial models and quantitative tools using Python (pandas, NumPy, scikit-learn) and Excel, supporting clients in making data-informed decisions related to investments and risk management.

• Developed algorithmic trading strategies leveraging predictive analytics, time series forecasting, and optimization algorithms, resulting in improved portfolio performance and alpha generation.

• Collaborated with cross-functional teams to structure and model sophisticated financial products using large-scale data and analytics.

• Applied advanced quantitative techniques, including stochastic processes, Monte Carlo simulations, and derivative pricing models, to evaluate and communicate the risk and value of financial instruments in dynamic markets.

Data Scientist (Finance)

Deloitte

Delivered comprehensive income tax compliance and advisory services to clients, overseeing tax compliance, managing individual tax returns, and ensuring adherence to local regulations.

•	Developed a new tax planning model in response to alterations in the Personal Income Tax Law, specifically Article 85, Paragraph 1, Item 17, known as the 'Independence Test' that provided strategic insights into the tax implications for entrepreneurs and flat-rate taxed entrepreneurs in the IT sector and increased the client base

Consultant

NCR Corporation

P&L ownership for Service Parts Operations in Germany, Switzerland, Austria, Spain and Portugal Oversaw 
management outlook and achieved a 100% month-end closing rate.

•	Identified and reported accruals/buffers, leading to a 28% reduction in financial discrepancies. Implemented advanced methodologies for financial modelling, resulting in a 15% reduction in quantified operational risks.
•	Enacted P&L modelling strategies in Q1 2019, leading to a significant reduction in operational costs for service parts operations across DAH and Iberia regions resulting in cost savings of $160,000 USD. Utilized Tableau and Power BI to generate data-driven recommendations, resulting in an incremental increase in efficiency within audited procedures.
•	Utilized Tableau and Power BI to generate data-driven recommendations, resulting in an incremental increase in efficiency within audited procedures.

Senior Finance Analyst

Elektromreža Srbije a.d. (Join Stock Company Elektromreža Srbije)

Led the preparation, review, and analysis of monthly (M), half-yearly (H), and yearly (Y) Profit and Loss (P&L) statements to assess performance against budgeted figures, providing actionable insights to senior leadership. 

• Revamped the P&L reporting structure to deliver greater clarity into revenue streams and cost centers, contributing to an 18% increase in profitability over two years. 
• Additionally, conducted workshops and training sessions on IT financial management best practices, particularly in P&L management, for cross-functional teams.
• As part of the strategic team, successfully spearheaded the upgrade of the SAP BPC system, which improved business process efficiency by 42% and enhanced overall operational performance by 33%. • Ensured the accuracy and relevance of all financial reports within the SAP CO and FI modules, supporting high-quality financial decision-making. 
• Modeled and delivered detailed financial plans and performance reports across M, H, and Y intervals.
• Developed a dynamic cash flow planning mechanism that improved cash flow efficiency by 18%, and conducted comprehensive cost-benefit analyses of key business and investment initiatives, resulting in approximately $1,000,000 in cost savings over two years. 
• Played a pivotal role in establishing system access tariffs, overseeing their implementation and evaluating their impact on operating results, specifically in the transmission of electric energy.

Finance Expert

Proud dad to a 3-year-old explorer.
I’m a hands-on technical leader with over 12 years of experience in capital markets, SaaS, asset management, and consulting. I specialize in building high-impact, data-driven systems across trading, finance, and fraud detection - delivering end-to-end solutions that span from data ingestion to AI model deployment and executive dashboards.

What I Do Best:

Backend Engineering: Python-first APIs with FastAPI/Flask, optimized for low-latency inference, Dockerized, and deployed with CI/CD, Kubernetes & Terraform.
Frontend & Data Apps: React, TypeScript, Power BI, and Tableau to translate complex models into usable, real-time insights.
Data Engineering: Spark, Airflow, DBT, Kafka, and Snowflake pipelines built for scale, observability, and analytics-readiness (Kimball modeling included).
Machine Learning & AI: Forecasting, classification, anomaly detection, and GenAI using PyTorch, scikit-learn, LightGBM, and LangChain applied to FX signals, fraud alerts, and market summaries.
Generative AI & LLMs: GPT-4o, Claude, Bedrock, and LangChain agents powering copilots, risk insights, and natural language interfaces on structured data.

Impact Highlights:

Cut FX transaction costs by $700K via Python/Spark optimization.
Delivered $2M+ in new revenue through AI product strategy and GenAI automation.
Improved fraud detection accuracy by 35% with real-time ML models.
Built FX signal generation pipelines with LSTM/Transformer models and real-time market integration.
Led 20–30 person cross-functional teams across global time zones.

Fluent in English, Russian, Serbian; conversational in French.
MSc in Quant Finance & Software Engineering, Stanford ML Honors, CFA Institute Member.

Let’s connect if you’re working on building intelligent systems in finance, trading, or enterprise AI. Always happy to talk ML, LLMs, or market tech.

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Aleksandar Mihajlovic, CFA's Linkedin Analytics

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