NVIDIA Jetson Experts in Germany

• TrendReel, production LLM agent and RAG system (Python, LangChain, OpenAI, Groq/Llama 3, Claude, FastAPI, Kubernetes, PostgreSQL).
• Designed and built a production multi-step LLM agent system: a script generation agent with a per-platform psychology database, 7 viral narrative frameworks, and structured quality scoring, switching between Claude and Groq backends in real time based on output metrics.
• Implemented multi-provider LLM routing (Claude primary, Groq/Llama 3 fallback) with priority-chain failover and quality-based provider switching, achieving 95% inference cost reduction while holding measurable quality thresholds.
• Built an advanced RAG-style retrieval pipeline with per-platform knowledge bases, semantic content matching, and structured output evaluation across 7 decision frameworks, directly analogous to multi-tenant context-based reasoning for enterprise document workflows.
• BrainyAI, adaptive AI learning platform (Python, LangChain, Groq Llama 3.3-70B, OpenAI, Next.js, Supabase, Redis).
• Integrated Groq Llama 3.3-70B with education-level-aware prompting, dynamically adjusting vocabulary depth, citation complexity, and reasoning style across four student proficiency tiers.
• Nexus Prime, multi-tenant SaaS platform for marketing and growth automation (25 modules, 99 backend routers, 153 frontend files).
• Built a 25-module, 99-router multi-tenant SaaS platform covering ad remix, affiliates, WhatsApp inbox, email, and cart recovery, serving four subscription tiers from $199 to $1,999 per month with integrated Stripe, Paystack, and Flutterwave billing.
• AI Video Surveillance Platform, multi-tenant edge and cloud computer vision system currently in active client pitch.
• Designed a multi-tenant AI video surveillance platform combining edge YOLO26 inference on NVIDIA Jetson Orin NX boxes with a central GKE cloud layer (Postgres, Pub/Sub, ClickHouse, R2, Keycloak) for event storage, dashboards, alerting, and multi-tenancy.

Leading the end-to-end development of Edge AI-powered smart glasses for visually impaired individuals, aligning product vision with user needs and managing a cross-functional team of data scientists, Android developers, AWS engineers, and hardware specialists.

• Defined product roadmap for Edge AI smart glasses and MVP features through user research, stakeholder interviews, and competitive analysis, ensuring accessibility and real-world usability.
• Developed and validated a PoC for AI-driven cancer cell identification in PET/CT scans, collaborating with medical experts to optimize diagnostic accuracy and clinical relevance.
• Built and scaled a multidisciplinary team of 75+ engineers, interns, and designers across Germany and India, driving cross-border collaboration and iterative prototyping.
• Established strategic partnerships with NGOs, healthcare providers, and advocacy groups to embed inclusivity and patient feedback into product design.
• Drove hands-on hardware-software integration using Raspberry Pi and Jetson Nano of AI models. Initiated and nurtured relationships with suppliers, manufacturers, and ecosystem players to build scalable go-to-market plans.
• Owned critical product decisions, from prototype development to funding strategy, applying a data-informed and impact-driven mindset.
• Fostered a learning-focused culture by facilitating brainstorming sessions and continuous feedback loops between engineering and product.

Achievements:

• Public speaker: Auto.Ai 2025 (Berlin), Wearable technologies 2025 (Munich and Bangalore), MEDICA 2024 (Dusseldorf)
• WMF, Bologna, Italy (June 2024): Only AI startup to be selected from Germany as EBV hero to represent sprhava on global platform
• Medica, Germany (2024): Delivered a speech on AI smart glasses in world's largest Healthcare event.
• Wearable Technologies, Bengaluru, India (Dec 2024): I was a speaker presenting sprhava and its product.
• Venturise Global Challenge (GIM 2025, Bengaluru Palace, Karnataka): sprhava was selected as one of the 16 top startups (ESDM) to present on this global platform
• Wearable Technologies Conference 2025 EUROPE, Munich, Germany (May 2025): Delivered a talk on Edge AI at the Europe's biggest wearable tech event.
• InsurNext Köln, Germany (2025): sprhava was honoured with a booth from Cologne administration.

Task: Test tool development for Smart-Home IoT-based heating control Test tool development for Smart-Home HVAC heating and ventilation. Eliminating memory leaks in C/C++ within the CAN Analyzer application. Reviewing Python and Tcl/Tk code. Reviewing C-based simulator code for BOSCH Home Comfort cascade heating control using Visual Studio Code and Conan/Conda. Reviewing Delphi application: conversion to 64-bit. Reviewing PyQt6 user interface. SiL testing of domestic water heater control for memory leaks. Stability testing and continuous operation. Migration to Windows 11 24H2. Developing device connectivity with WinSock2 (WSAAPI, recv, send, select) for EMS/CAN IoT communication with C. Testing with virtual machines and Server Manager. Writing reports about the test cases. Logging with the RoboTT framework. Creating test procedure documentation with Docupedia. Bare-metal programming of embedded STM32 Cortex-M and simulation with Renode.io. In-depth familiarization with the SEGGER EmbOS firmware framework and its related system calls. Reviewing MATLAB/Sinumerik .m program files. Implementing redesigned OS schedulers in SEGGER EmbOS.Technologies: Embarcadero RAD Studio 10.2, Visual Studio Code, Sourcetree, Conan, Cmake, Conda, EmbOS, PyQt6, Qt6, Delphi VCL 32-bit, CAN, EMS, Powershell, MingW64, STM32 ARM-Cortex-M4F Renode.io, Windows Server 2022 Standard, RoboTT, IoT

Development of a route planner for incident communication.

• Development of the REST API
• Set up a patch system for maintaining the routing graph
• Expansion of the testing infrastructure
• Performance and memory optimization (JMeter, JFR)

Technologies: Java 21, Spring Boot, JGraphT, Flyway, MapStruct, Caffeine, ShedLock, JMeter, Kubernetes, JFR

• Automated driver assessment by programming temporal rule engines to evaluate lane-change execution safety, head-pose mirror checks, indicator usage cycles, and compliance with traffic lights and road signs
• Synchronized real-time traffic sign recognition and multi-state traffic light classification models with time-series CAN-bus telemetry and HD-map spatial priors to grade traffic rule adherence
• Trained and deployed distinct deep learning models optimized for interior cabin monitoring and exterior surrounding-area perception
• Combined perception outputs with camera intrinsics and horizon stability checks to execute 3D ground-plane object distance estimation assuming flat-ground geometry
• Deployed a split-compute edge network across a 10-vehicle fleet via VPN, implementing a zero-allocation host memory pipeline to eliminate frame accumulation latency (6×21 FPS per vehicle)

Industry Software from Route to Invoice

In Production

In use at: landscaping company, two home care services

The entire workday, digital: sign-in, route plan on the vehicle, departure checks, photo documentation, time tracking, invoicing. The tablets keep working offline and sync afterward. Records are immutable, making time tracking easier to audit. Runs on a dedicated server in Germany with continuous monitoring and remote updates.

Proof: In daily use at the landscaping company with six vehicles since September 7, 2026.

• React Native on Android tablets
• Python
• SQLite
• Two-way synchronization
• Linux server in Germany
• Remote maintenance

• Implemented data/feature pipelines and automated validation/reporting for multi-million-row insurance datasets.
• Built production-grade image processing to detect fabric defects; delivered maintainable, well-tested Python code and concise reports.

• IT consulting, coaching, and implementation with a focus on AI

• Team leadership: Prioritization and coordination of four cross-functional teams.
• Platform strategy: Development and implementation of strategies to optimize existing IT platforms.
• Stakeholder management: Active management of expectations and communication with internal and external stakeholders.
• Program and innovation management: Prioritization and coordination of cross-department projects as well as innovation initiatives.
• Product Owner consulting: Advising Product Owners with a focus on product development and continuous product improvement.
• Organizational development: Improving communication and decision-making structures across all organizational levels.
• Change management: Implementing best-practice change management methods to ensure continuous optimization and innovation.
• Quality assurance: Ensuring high quality standards in processes, services, and deliverables.

• Developed a real-time video analytics proof-of-concept for deployment on NVIDIA Jetson edge devices.
• Implemented DeepStream pipelines including object detection, tracking, human pose estimation, face anonymization, and zone intrusion detection.
• Built a Qt/Python demonstration UI interfacing with the AI pipeline via REST APIs.

• Developed AUTOSAR applications using Eclipse RCP for RTA-CAR AUTOSAR stack from ETAS
• Collaborated with Platform and Isolar-A teams on ASW development and testing workflows
• Implemented UI automation testing using Java SWTBot framework, enhancing regression coverage
• Built microservices-based applications using Java, Spring Boot, and Angular for internal tooling
• Enhanced AUTOSAR toolchain reliability through cross-team collaboration in development and testing

• Developed a Python-based synthetic data generation pipeline in Blender to simulate complex human-forklift interactions for robotic perception and AI model training.
• Designed and modeled 3D industrial digital twins to support depth estimation, stereo vision, and safety analysis workflows.
• Collected and processed LiDAR, laser, and photogrammetry point clouds to generate accurate 3D maps for environment reconstruction and ground-truth data creation.
• Developed and deployed YOLOv8-based pose estimation and depth perception algorithms using PyTorch and OpenCV, optimized for GPU clusters and NVIDIA Jetson platforms.
• Integrated and validated AI modules in ROS-based robotic environments, ensuring real-time performance and interoperability.

• Computer vision engineer responsible for development of industrial vision solutions, beginning as a working student and transitioning to a full-time role in May 2025.
• Designed and implemented vehicle detection and counting models; integrated the pipeline into a cloud-deployed system (Azure) that delivers live analytics dashboards.
• Building an offline print quality assurance system that scans corrugated-board prints on production lines to detect and classify defects such as splashes, impurities and colour deviations, deploying the solution on Jetson edge devices.
• Collaborated with cross-functional teams while focusing on computer vision components, containerization, and deployment.

• Developed LLM-based automation utilities including structured reasoning pipelines, LLM-as-a-Judge evaluation tools, and multi-model comparison frameworks.
• Built RAG pipelines for internal research workflows using LangChain, ChromaDB, and FastAPI, enabling semantic retrieval and multi-step reasoning.
• Integrated LLM microservices into existing ML systems using Docker, FastAPI, and GitLab CI/CD with reproducible deployment workflows.
• Designed inference APIs combining vision models and LLM reasoning for multimodal analytics and decision-making.
• Optimized embedding-based retrieval using vector store pruning, improved chunking logic, and dynamic retriever selection.
• Performed prompt engineering and system instruction tuning for consistency, robustness, and reasoning quality.
• Built benchmarking suites to evaluate LLM latency, reasoning quality, retrieval accuracy, and robustness under different prompt templates.