LiDAR Experts in Germany

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

• Led the hardware development and validation of a safety-critical 400-V battery management system (BMS) for the TOGG SUV program; performed system architecture reviews, schematic validations, EMC and reliability tests, and root cause analyses in compliance with relevant automotive standards (ECE-R10, CISPR25, ISO-26262). Coordinated cross-country EU engineering teams, customer reviews, and technical documentation to deliver a production-ready, validated system.

• Architected and integrated 22 state-of-the-art ADAS validation vehicles for BMW ADCAM and LIDAR programs at Magna Electronics; synchronized up to 26 heterogeneous sensors (LIDAR, RADAR, cameras, GNSS/INS) using PTP-based timing architectures. Defined the system architecture, HW/SW interfaces for hardware-in-the-loop (HIL) rework, sensor integration strategies, and data acquisition frameworks. This enabled scalable vehicle-level validation, improved validation efficiency by 25%, and reduced project costs by €1.45 million.

• End-to-end validation and integration of automotive radar platforms for a Daimler project at Continental. Developed automated open-loop hardware-in-the-loop (HIL) environments to accurately test target tracking KPIs, field-of-view limits, and thermal and voltage-related ECU state machines. Skilled use of a highly complex toolchain consisting of CANoe, Lauterbach Trace32, RADAR target simulators, and EMC shielding chambers for RF and system validation. Synchronized global, interdisciplinary teams to speed up troubleshooting and close critical technical gaps.

• Reconstructed and validated the product architecture of an electromechanical e-bike by integrating and troubleshooting critical subsystems (BMS, motor control, sensors, HMI, electronic locking systems). Built a comprehensive system-level test bench for functional testing, fault reproduction, and performance analysis; coordinated suppliers and implemented corrective actions to improve reliability and traceability.

• Defined the system architecture and validation strategy for a LIDAR platform developed in cooperation with Elmos Semiconductor; evaluated optical measurement concepts, SPAD detector integration, and system requirements, and created technical recommendations for product development and verification.

• Developed LabVIEW-based automation and verification software for a high-precision hydraulic and electromechanical test system for FTE Automotive; integrated NI DAQ hardware for high-frequency real-time capture of physical measurements. Developed automated test sequences, programmable endurance tests, troubleshooting routines, data logging, and analysis tools to improve test efficiency and traceability.

• Successfully delivered full engineering life cycles for well-known industrial customers (Magna, Continental, Farasis, FTE Automotive, BMW, Daimler, TOGG) – from requirements engineering and proof of concept to system integration and validation, technical documentation, supplier coordination, and user training.

• Developing a model-based AI where natural language is interpreted as formal relations.
• Natural language terms are not considered rigid but fluid and can be negotiated in a context so meaning resolves by iteratively specifying.
• Develops all kinds of model solutions.
• Backed by natural language and data annotation.
• Requirements to code and other solutions.

• Building strong awareness within the company around nearshoring
• Engagement Manager in the Nearshore Competence Center
• Responsible for nearshore consulting, partner screening and technical onboarding, designing and implementing cooperation scenarios, change management, stakeholder management, participation in steering committees, and collaboration with IT and business units
• Tools used: Microsoft Office 365, Microsoft Teams, Azure Devops, Microsoft Sharepoint, Conceptboard
• Key results: Establishment of a nearshoring strategy, successful identification and implementation of outsourcing partnerships, delivery of change management and stakeholder management at the highest level

• Responsibility for the further development of digital service products with a focus on customer value, profitability, and scalable growth
• Development and prioritization of the service product roadmap based on customer feedback, market analysis, financial evaluation, and technical feasibility
• Identification of new digital service opportunities as well as derivation of product requirements, feature priorities, and value propositions
• Alignment of product vision, roadmap, and user experience with product owners, center of competence, sales, service, and market organizations
• Support of go-to-market activities, market validations, and internal training for the successful launch of digital service offerings
• Use of data-based decision-making to assess customer needs, business model assumptions, and product progress

• Directed full lifecycle of embedded software projects including LiDAR, BMS, OCU, and Cybersecurity systems.
• Coordinated internal and external stakeholders including suppliers like Bosch, Bertrandt, Magna, Continental and IAV.
• Managed cross-country teams of 12–15 engineers, conducting project onboarding and role-based training.
• Implemented change control and risk registers for safety-critical automotive platforms.
• Oversaw project schedules using MS Project, JIRA, and Azure DevOps to ensure milestone alignment and transparency.
• Extensive hands-on expertise in test planning and execution for ECUs in compliance with Automotive SPICE (SWE.4–SWE.6) and ISO 26262.
• Led digital transformation project with a cross-functional team of 15+, ensuring successful integration of systems and workflows.
• Maintained project overviews and tracking in tools like Milestones (Daimler) and MS Project, and managed status reporting to senior stakeholders.
• Coordinated with third-party transformation partners and ensured timely delivery of decision templates and risk assessments.
• Applied PRINCE2 methodology for structured delivery; facilitated agile ceremonies and maintained Confluence-based documentation.
• Strong leadership in cross-functional teams and working in agile environments (SAFe, Scrum).
• Successfully led and supported IT and transformation projects, including those in regulated sectors (e.g., KRITIS or energy sector).
• Represented or supported overall project leadership in large-scale transformation programs.
• Maintained and updated complex project plans (Gantt charts, milestone tracking, and resource allocation).
• Coordinated workstreams and interdisciplinary teams; ensured timely delivery of all work packages.
• Acted as central communication point between project team, senior management, external vendors, and transformation partners.
• Prepared and delivered concise status reports, steering committee presentations, and decision memos.
• Identified project risks, maintained risk registers, and led mitigation planning.
• Assessed resource needs and supported reallocation based on project priorities and constraints.
• Proficient in JIRA, Milestones, Confluence, MS Project, MS PowerPoint, and Excel.
• Used both Agile (Scrum) and Waterfall (PRINCE2, PMI) methodologies.
• Created and tracked action lists, meeting protocols, and backlog items.
• Conducted critical analysis of project progress, resource bottlenecks, and deliverable timelines.
• Ensured alignment of transformation goals with enterprise IT architecture and business objectives.

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.

• Development and maintenance of IT architectures with embedded Linux systems.
• Designing, implementing, and optimizing backend applications and script-based solutions.
• Analyzing and resolving issues, including troubleshooting and user support.
• Developing and implementing security concepts for cloud solutions.
• Administering networks (DHCP, DNS, NTP, VPN).
• Technologies: Linux, PowerShell, Bash, Python, Ansible, Kubernetes, GitLab CI.
• Methods: Kanban.

• Developed and implemented AI-based perception and multimodal systems for real-world environments
• Built, trained, and evaluated Machine Learning and Deep Learning models using Python, PyTorch, TensorFlow, and OpenCV
• Worked with Vision-Language Models (VLMs), Large Language Models (LLMs), transformer-based architectures, and multimodal AI systems
• Applied LoRA-based fine-tuning techniques and experimented with diffusion models for generative and multimodal AI applications
• Developed multimodal perception pipelines using camera, LiDAR, and sensor data
• Designed end-to-end workflows for data processing, model training, evaluation, benchmarking, and robustness analysis
• Utilized HuggingFace Transformers and modern Deep Learning frameworks for AI experimentation and deployment workflows
• Applied GPU-accelerated computing, CUDA-based processing, ONNX, and TensorRT optimization for efficient inference and large-scale model training
• Collaborated with industry partners including Valeo and REHAU on applied AI and intelligent system projects
• Developed scalable AI architectures and prototype software solutions for automation and perception tasks

• Engaged in a structured career development program while strengthening German language proficiency (B1 level) and evaluating opportunities in ADAS/AD systems and requirements engineering.

• Lead and coordinate complex cross-functional projects within LiDAR and software development environments.
• Develop and maintain critical project schedules, monitor work packages, track project baselines, and manage milestone progression via Gantt plans.
• Drive robust project controls by preparing detailed project forecasts, cost analyses, workload planning, and resource tracking.
• Monitor and report core KPIs related to cost, timing, quality, and delivery performance to ensure alignment with organizational goals.
• Design and present executive dashboards, management reports, and steering committee presentations for senior leadership.
• Facilitate seamless communication and alignment between R&D, Quality, Purchasing, Manufacturing, Finance, and international stakeholders.
• Proactively identify risks, maintain comprehensive risk registers, and contribute to mitigation planning and escalation processes.
• Coordinate engineering change requests, assessing their operational impact on project scope, timing, resources, and budgets.
• Drive efficiency through AI-powered process improvements using LEA Suite, structured prompting, workflow automation, and automated meeting documentation.

• Topic: Object Detection and Semantic Segmentation for (AUV) Systems using Transformer-Based Vision Models and Sensor Fusion.
• Designed and implemented an end-to-end multi-sensor fusion perception pipeline (Camera, LiDAR, IMU) in ROS
• Developed CNN-based Machine Learning model (YOLOv8) and Transformer-based vision models for real-time object detection
• Processed and clustered 3D LiDAR point clouds using DBSCAN, RANSAC, and voxel grid filtering to enable robust object localisation in noisy environments.
• Designed Bayesian Network models (GeNle) for probabilistic reasoning and sensor-level decision fusion under uncertainty.
• Applied Kalman filtering for sensor state estimation, temporal alignment, and smooth object tracking, reducing false positives in safety-critical scenarios.
• Evaluated system performance under realistic driving dynamics, improving tracking stability and overall perception robustness.
• Built deep learning pipelines for training, validation, and performance evaluation of perception models using sensor data.

• Built a Trend-Scouting AI system to automate technology intelligence in power electronics and semiconductors, combining Azure OpenAI with LangChain, Scrapy-based web crawling for structured, noise-free data acquisition, and automated PDF reporting for internal R&D use. Developed a FastAPI-based (Uvicorn) web application to validate LLM outputs, test prompt strategies, and enable interactive system evaluation.
• Developed a real-time STM32 binary telemetry debugger with a PyQt-based GUI, featuring header-based frame synchronization, anomaly detection, template-driven payload decoding, time-aligned buffering, and live signal visualization.
• Developed an AI-driven power inductor designer using surrogate regression models for accurate electromagnetic and thermal prediction. Integrated multi-objective NSGA-II optimization to generate efficient, manufacturable designs.

Taskforce leadership for the development of a central ECU for autonomous driving levels 23 and 4