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Are you a B2B Tech Founder or C-level executive whose brilliant product is losing deals to competitors with weaker tech?

The reason is not your engineering, it’s a Visibility Gap.

You have the technical expertise and the solution; you just lack the high-impact system to make your brand visible, authoritative, and impossible to ignore among your ideal buyers.

Introducing the High-Impact Visibility System (HVS):

I help turn complex technical IP into high-performing content that generates qualified pipeline. My proprietary High-Impact Visibility System (HVS) is a proven, battle-tested framework for technical leaders who are ready to scale:

Problem-to-Post: 
Translating dense technical insights into content pillars that resonate immediately with executive-level buyers.

Impression Scaling: 
Going from under 2K to 100K+ impressions on LinkedIn, ensuring you own the mindshare in your niche.

Authority Building: 
Positioning you and your team as the definitive experts, turning visibility into genuine sales conversations and closed deals.

We do this efficiently. 
I implement the engine, you own the expertise: it only takes 1 hour of your time per week.

The Results:
I've helped my clients launch content engines that:

- Put them in front of 100,000+ potential target customers.
- Generate predictable inbound leads and fill their pipeline with quality buyers.
- Drove 10X growth in qualified search appearances and profile views in 90 days.

Ready to close the Visibility Gap and make your brand the category leader?

👉 Book your free 30-minute Strategy Call below to get my exact playbook for scaling B2B Tech CEOs to 100K+ impressions: https://calendar.google.com/calendar/u/0/appointments/schedules/AcZssZ1PfD5Q4fwUfAzJqKB67XsGdhWvE9XfchfWYRpiw2q9Vba57xRUXpSjogtHFm6FBiNVvUo4eYYR

Lukas Timm

RadiusAI

Senior Applied Scientist

𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 :-
• Real-time Instance Segmentation - Performed R&D in replacing YOLOv8 Instance Segmentation model with an efficient custom model to facilitate the removal of AGPL 3.0 License for real-time application. The qualitative and quantitative results, both, were superior to YOLOv8 in segmenting each individual SKU in a given frame.
• Loss function optimization - Led R&D efforts to optimize the existing loss function for classification. Improved the model's generalizability and accelerated convergence resulting in reduction of training time by more than 60% on a single GPU by just replacing the existing loss function with the proposed one.
• Continual Learning - Researched and developed Domain-Incremental and Class-Incremental Continual Learning techniques, delivering substantial scalability improvements. Successfully slashed training time by over 80% for new SKU classifications, maintaining exceptional accuracy for existing SKU categories.

𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝗻𝗲 :-
• Developed a robust, scalable PyTorch training framework tailored for diverse models, datasets, and training scenarios (e.g. Distributed Training, Knowledge Distillation, SSL, Continual Learning). Streamlined operations, reducing development cycles by more than 10x and enhancing overall training efficiency.

Deep Learning Engineer

Tiger Analytics

• Discussed current trends in AI with Data Science Leads and Director. Worked on implementing the same as part of Internal Computer Vision Platform.
• As part of internal Computer Vision team, wrote various blogs on topics like Camera calibration, Self-supervised learning, Object tracking, Multi-task network.
• Mentored and guided juniors on various computer vision projects for client use cases.

Machine Learning Engineer

• Worked on a project for one of the largest Retail Companies in the US where I have built various backend APIs on Azure as per the business requirements. Secured the APIs using JWT.
• Worked on implementing Databricks workloads and improved existing workloads by optimizing them for cost and memory.
• As part of Tiger's Internal Computer Vision Platform, developed various modules while maintaining high coding standards and curated it to be used on different datasets, on the following topics:-
a) Knowledge Distillation:
Distilled large models across different tasks to optimize for less memory and low latency.

b) Pruning and Quantization:
Explored various pruning techniques and created a custom pipeline to Prune the CNN networks efficiently. Developed scripts for performing various Quantization techniques for CPU and GPU using frameworks - ONNX, Intel OpenVINO, Nvidia TensorRT.

c) Multi-Object Tracking:
Explored and compared 7 different MOT trackers for speed that are released from the year 2016 - 2023. Developed scripts that can be used for seamless integration of different MOT trackers with the object detection algorithm of choice. This led to faster development and experimentation of different trackers for client use cases.

Senior Analyst - Machine Learning Engineer

Avantari Technologies

• Worked with the team on Research and Development of training efficient networks in PyTorch.
• Worked on optimizing deep learning models using Intel OpenVINO and improved the inference latency by 60%.
• Deployed the model on AWS serverless computing platform using light-weight runtime.
• Worked on implementing a conversational bot using AWS Lex and integrating it with the AWS serverless platform.

MSys Technologies | Software Product Engineering Services

• PoC to Production - Design, develop, deploy and secure the serverless application built entirely using AWS services that are being used by thousands of users.
• Design and develop customized and scalable AWS solutions as per business requirements.
• Processing large files using a serverless computing platform while building a robust solution to handle the serverless compute timeout limitations.
• AWS services - API Gateway with Custom OKTA Authorizer, Lambda Functions-Python3, Step Functions, EC2, VPC, RDS, S3, SNS, SQS, EFS, CloudFormation

Software Engineer

Experienced and results-driven Machine Learning & Software Engineer with a robust background spanning 4 years, navigating diverse technologies and industries encompassing both service and product domains. My passion lies in the intricate realm of Computer Vision, where I possess a deep understanding of camera imaging principles and fundamentals.

My expertise extends across a spectrum of advanced techniques, including Multi-Task Learning, Self-Supervised Learning, Continual Learning, Kalman Filters, and Object Tracking. I specialize in Image Segmentation, Optical Flow, Stereo Vision, Depth Estimation, and Sensor Fusion involving both Camera and LiDAR technologies.

Adept at crafting and implementing custom neural networks and distributed training, my skills shine in optimizing models for reduced memory consumption, compact size, and low inference latency. I bring to the table proficiency in cutting-edge techniques such as Knowledge Distillation, Pruning, and Quantization. My deployment capabilities encompass various runtime accelerators, including ONNXRuntime, Intel OpenVINO, Nvidia TensorRT, and the Nvidia Triton Inference Server for seamless serving of single or ensemble of models.

Beyond Computer Vision, I excel in backend application development using FastAPI, ensuring scalability and performance. My proficiency extends to deploying applications seamlessly using cloud-managed CI/CD pipelines, Docker, and Kubernetes.

I am passionate about pushing the boundaries of what's possible in the world of Machine Learning and Computer Vision, continually seeking innovative solutions to complex challenges.

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Lukas Timm

Dheeraj Madda

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