Akash Gupta

EXECUTIVE SUMMARY

Engineering leader with 12 years of experience building large-scale data platforms, microservices, and applied GenAI systems. Currently a Vice President at BlackRock, leading high-impact initiatives in public markets data acquisition and enterprise automation. Skilled at driving strategy, shaping architecture, and delivering production-grade AI solutions.

Hands-on with OpenAI GPT models, Mistral, cloud platforms, prompt engineering, API orchestration, and model evaluation. Known for taking ambiguous, high-stakes problems, breaking them down, and driving them to completion with clarity, speed, and technical judgment. Comfortable leading teams of 10+, mentoring interns at scale (170+), and influencing cross-org decisions.

CORE SKILLS

GenAI: OpenAI GPT models, Mistral, prompt engineering, RAG fundamentals, model evaluation, API orchestration, automated quality pipelines
Backend & Systems: Python, Java, Perl, microservices, REST APIs, distributed systems, data pipelines, high-traffic ingestion platforms
Leadership: Org-wide strategy, architectural direction, delivery ownership, mentoring, stakeholder management, incident response
Data Platforms: Data ingestion, validation, normalization, compliance frameworks, scalable infra, workflow orchestration
Cloud: Azure (AZ-900 certified), hands-on with cloud-native patterns
Other: Automation, performance tuning, CI/CD, infra modernization.

GENAI PROJECTS

1. WikiAI Chatbot – Enterprise Knowledge Summarization

Designed and built a Python-based GenAI chatbot that retrieves and summarizes information across large documentation wikis.
Impact: Eliminated manual browsing effort, reduced search time dramatically, and improved onboarding speed for engineers.
Responsibilities:
Built prompt frameworks for precise, context-aware answers
Integrated multi-document retrieval and summarization
Created evaluation workflows to measure answer quality
Optimized API calls for latency and cost

2. DataGenie – Automated Data Recommendation System

Developed a GenAI system that auto-recommends missing or ambiguous values in input data files using sender patterns, historical context, and model-driven inference.
Impact: Reduced rejection rate for incoming data, improved accuracy, and cut manual corrections.
Responsibilities:
Designed the end-to-end workflow for automated data enrichment
Implemented model-driven conditional prediction logic
Built evaluation mechanisms to validate recommendations
Drove adoption across teams

3. Internal GenAI Utilities (Various POCs)

Implemented automated code-generation experiments from client specs
Built prompt templates for structured output generation
Evaluated Mistral models for cost-effective internal workloads

FINANCIAL MODELING & MARKET SYSTEMS PROJECTS

Over the last seven years, delivered and led multiple large-scale financial modeling and market-data engineering projects across public and private markets using Python, Perl, Java, and C++. These initiatives supported valuation workflows, high-volume data ingestion, security master enrichment, pricing pipelines, and compliance reporting. Worked extensively on integrating vendor feeds, normalizing disparate asset-class data, and building the underlying logic that powers portfolio analytics and risk engines. Contributed to architecture decisions, performance tuning, pipeline modernization, and incident recovery for mission-critical market systems used throughout the firm.

Worked across three companies in full-stack roles, building web, mobile, and backend solutions using a diverse technology stack. Gained strong hands-on experience with PHP, Bootstrap, Angular, React, Sencha, Apache, and Android Studio, while also contributing to blockchain-related development work. This phase shaped core engineering fundamentals across UI development, API integration, and scalable application building, forming the foundation for later enterprise-level engineering leadership.