Pranay Modukuru

Senior AI Engineer Sep 2025 – Present

• Shipped production agentic-RAG pipeline (LangGraph, FastMCP) with query planning, parallel retrieval, reranking, stable citations, and configurable reasoning effort, with multi-provider LLM support.
• Authored foundational ADRs on multi-tenant access control, billing, and enterprise MCP integration, drove adopted architecture decisions across three consuming platforms without formal mandate.
• Own product strategy and roadmap for the platform, initiated the Agentic AI product direction and the Data Access Layer vision, aligning engineering and business stakeholders across teams.
• Built enterprise MCP tools for chat, knowledge-base configuration, and retrieval, supporting machine-to-machine and on-behalf-of auth flows.
• Built LLM-model provider-neutral API contract to enable flexible integration with multiple LLM providers.

AI Engineer Mar 2025 – Aug 2025

• Built an API-first RAG-as-a-Service platform (Azure AI Search hybrid + semantic retrieval, Document Intelligence) adopted across multiple Deutsche Bahn business units.
• Engineered a multi-provider LLM abstraction layer via a dynamic factory pattern, enabling model swaps without changing downstream pipeline stages.
• Designed the multi-tenant security architecture with auth-claim validation, session guardrails, and multi-layer deletion protection, guaranteeing complete data isolation across tenants.
• Authored OpenAPI specifications and maintained Azure APIM gateway policies across dev, integration, and production.

Machine Learning Engineer Sep 2022 – Feb 2025

• Automated versioning and multi-environment deployments with GitLab CI, FluxCD, and Helm, eliminating manual release overhead.
• Provisioned scalable AWS EKS clusters and storage with AWS CDK, delivering identical, production-ready environments.
• Orchestrated automated ML training and evaluation pipelines with Prefect and MLflow, improving experiment tracking and collaboration.
• Standardized Docker images and DevSpace templates, reducing developer onboarding from days to hours.
• Designed and ran technical training workshops to upskill the team on newly adopted infrastructure and workflows.

Senior AI Researcher Jan 2022 – Aug 2022

• Developed a novel time-series domain adaptation algorithm to scale predictive models across machines, securing US Patent No. US12032929B2.
• Designed automated root-cause and prescriptive-analysis AI features with product teams and domain experts.
• Supervised three AI researchers and an intern building scalable data processing algorithms for plant-wide AI rollouts across two manufacturing facilities.
• US Patent · US12032929B2 · Granted Jul. 2024 — System and Method for Cross Domain Generalization for Industrial Artificial Intelligence Applications
• EP Patent · EP4254087B1 · Granted Mar. 2026 — System and Method for Recommending a Recipe in a Manufacturing Process
• EP Patent · EP4300229A2 · Application Pending — System and Method for Determining Defect Regions of Products in a Manufacturing Process

AI Researcher Sep 2020 – Dec 2021

• Deployed a predictive model prescribing optimal casting parameters for a wheel manufacturer, reducing scrap by 50% and saving €144k annually.
• Developed an automated time-series framework standardizing data integration and feature engineering, cutting deployment timelines from 50 to 30 days.
• Built proofs-of-concept for 5+ predictive maintenance and quality-optimization use cases, driving AI adoption for manufacturing clients.

Research Assistant — Machine Learning Feb 2019 – Aug 2020

• Developed deep learning algorithms and feature pipelines for multivariate sensor data, optimizing computer vision and predictive maintenance systems.
• Developed a novel regularization technique and multi-headed neural network, reducing catastrophic forgetting from 6.5% to 1.0% on sequential classification tasks.