AI Image/Video Engineer

@MashoodurRehman1

I am an experienced AI/ML engineer with over 5 years of hands-on experience in designing and optimizing AI image and video generation systems. I have a strong proficiency in PyTorch, with extensive experience using Diffusers and DiT architectures. My background includes optimizing GPU inference performance, utilizing TensorRT, xformers, and advanced quantization techniques to achieve high-efficiency results. In my recent projects, I have successfully integrated and fine-tuned open-source video generation models, ensuring high-concurrency inference for reliable production environments. I have practical experience implementing LoRA-based fine-tuning, focusing on hyperparameter tuning and managing overfitting to maintain character consistency and style customization. Additionally, I am adept at building and deploying inference API services using both Python and Node.js, enabling seamless transitions from research to production. I am keen to explore how my skills align with your project's specific needs and would welcome the opportunity to discuss further. If there are any additional details you require, please feel free to reach out.

@gunslinger

This looks like a strong research to production gap problem, not just model work. While my background is more on backend and system architecture rather than deep model training, I’ve worked on integrating AI services into real world applications where reliability, scalability, and clean API design matter just as much as the model itself. For a setup like this, I’d focus on structuring a stable inference pipeline, handling request concurrency, and making sure the system can scale without breaking under load. That includes designing clean API layers, managing queues for GPU workloads, and ensuring deployments are predictable and maintainable. If you already have models or pipelines in place, I can help turn them into a production ready system that’s easier to operate and extend. Happy to discuss where you are currently and what’s blocking you most.

@ZohaibRoy

⭐⭐⭐⭐⭐ Optimize AI Video Generation Pipelines for High-Concurrency Inference ❇️ Hi My Friend, I hope you are doing well. I reviewed your project needs and see you are looking for an AI/ML engineer to design and optimize video generation pipelines. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in AI image and video generation. I will implement efficient methods to enhance performance and provide added value within your budget. ➡️ Why Me? I can easily handle your project as I have over 5 years of experience in AI/ML engineering, focusing on video and image generation systems. My expertise includes PyTorch, GPU optimization, and fine-tuning models. Additionally, I have a strong grip on deploying inference API services using Python and Node.js. ➡️ Let's have a quick chat to discuss your project in detail and I can show you samples of my previous work. Looking forward to connecting with you in chat. ➡️ Skills & Experience: ✅ AI/ML Engineering ✅ Video Generation ✅ Image Generation ✅ PyTorch ✅ TensorRT ✅ xformers ✅ Quantization ✅ LoRA Fine-Tuning ✅ Model Optimization ✅ Inference API Development ✅ Hyperparameter Tuning ✅ Collaboration with Researchers Waiting for your response! Best Regards, Zohaib

@irsolutions

With over a decade of experience in AI/ML engineering and high-performance systems, I understand the importance of designing and optimizing AI image/video generation pipelines for high-concurrency inference, as outlined in your project goals. My background in scaling projects for over 1 million users and expertise in GPU inference optimization directly align with the challenges of integrating and fine-tuning open-source video generation models like Wan2.2 and CogVideoX. One strategic insight I can offer is to implement LoRA-based fine-tuning for character consistency and style customization, leveraging my past success in building and scaling Telegram Mini Apps. Also, this approach ensures not only accuracy but also customization for your specific needs. I encourage you to reach out so we can discuss the roadmap for your project in further detail. My proven track record in AI/ML engineering, video/image generation systems, and GPU inference optimization positions me as the ideal candidate to help accelerate your path from research to production.

@ludiac

Hi, I’ve worked on AI systems involving high-throughput inference pipelines and LLM-based architectures, and I’m comfortable optimizing performance at both model and infrastructure levels. While my recent focus includes LLM agents and data pipelines, I also have hands-on experience with PyTorch, model optimization (quantization, batching), and deploying scalable inference APIs. I understand the challenges of moving from research to production and building reliable, high-concurrency systems. Relevant AI projects: https://www.freelancer.com/projects/php/OpenAI-Prompts-for-Telco-Support/reviews https://www.freelancer.com/projects/gpt-agent/Data-Analyst-Required/reviews https://www.freelancer.com/projects/php/Sharepoint-RAG-SQL-GPT-agent/reviews https://www.freelancer.com/projects/php/SQL-RAG-GPT-Agent-with/details https://www.freelancer.com/projects/python/Python-Data-Analysis-Script-39438040/reviews https://www.freelancer.com/projects/installation/Python-FastAPI-Coach-Help-Apply/details Happy to discuss your pipeline and optimization goals in detail. Thanks.

@Softeria

✅ Proposal for AI Image/Video Enhancement With over 5 years of AI/ML engineering experience, I am adept at designing and optimizing AI image/video generation pipelines for high concurrency inference. My proficiency in integrating and fine-tuning advanced video generation models like Wan2.2 and CogVideoX aligns seamlessly with your project requirements. I have a strong background in PyTorch and am skilled in GPU inference optimization using TensorRT, xformers, and quantization techniques. Additionally, my experience with LoRA-based fine-tuning ensures character consistency and style customization. I am confident in my ability to collaborate effectively with ML researchers and accelerate the journey from research to production. Let’s advance your project with innovative solutions and expert integration.

@AITSoft

Hello, I understand you need an AI image/video engineer who can design and improve pipelines for fast, high-volume AI video and image generation. I have experience integrating and fine-tuning open-source video models and can optimize GPU use through methods like TensorRT and quantization. I’m familiar with LoRA fine-tuning to keep characters consistent and customize styles. I also can build inference APIs using Python or Node.js, and have worked closely with researchers to bring projects from research to production smoothly. My approach is to focus on efficient pipeline design, thorough model tuning, and solid deployment to meet your needs. Could you share which specific open-source models you prefer besides Wan2.2 and CogVideoX? Thanks, Best regards, What are the main performance goals or targets you want to achieve with GPU optimization for the video/image generation? Could you specify your preferred platform or cloud environment for deploying the inference API services? Are there specific datasets or character sets you want the LoRA fine-tuning to focus on? Do you have any current bottlenecks or issues in your existing pipelines that must be addressed? What is your timeline for integrating the models and having a production-ready system?

@shadabkhan92

As a seasoned AI engineer with over 5 years of experience, I provide so much more than just building models or optimizing pipelines. I offer you the rare combination of skills your project needs; AI/ML engineering, video/image generation systems, and GPU inference optimization - all handled Python/Node.js languages. I understand and have hands-on experience with open-source models like Wan2.2, CogVideoX, Diffusers, and DiT architectures. Trust in my familiarity with frameworks like TensorRT and xformers to boost the GPU inference performance. One area where I stand out is my mastery in LoRA fine-tuning; hyperparameter tuning, overfitting control included. In a project like yours that demands character consistency and style customization, this invaluable skill plays a massive role. Not only am I comfortable working independently but also collaborating efficiently with ML researchers. My commitment to delivering on time and within budget will simplify our synergy for a better outcome.

@quan9824

Hi, This aligns very closely with my experience building production-grade AI generation systems and high-performance inference pipelines. I’ve worked on image/video generation workflows (Diffusers, DiT-based models) with a strong focus on latency, scalability, and GPU efficiency—not just research prototypes. What I bring: End-to-end pipeline design for high-concurrency inference (queueing, batching, async orchestration) Hands-on with Diffusers, PyTorch, and custom model pipelines, including multi-GPU setups GPU optimization: TensorRT conversion, xformers, mixed precision, and quantization strategies to reduce latency and cost LoRA fine-tuning: style/character consistency, hyperparameter tuning, and preventing overfitting in production scenarios Experience turning research models into stable API services (Python/FastAPI or Node.js), ready for real-world usage Relevant work: Built scalable pipelines for multi-person image generation with identity preservation and low-latency constraints Optimized inference stacks to meet strict response-time targets under concurrent load Designed modular systems to plug in new models without breaking production I’m comfortable collaborating closely with researchers while ensuring the system remains production-ready, efficient, and maintainable. Happy to discuss your current stack and identify quick wins for performance and scalability. Best regards, Doan

@FaizalShaik80

Hello, I can deliver what you need. I went through your project details and found that I worked on almost the exact same task about two months ago. I am a skilled freelancer with 6+ years of experience in Python, Machine Learning (ML), Node.js and I can deliver the results as quickly as possible. You can visit my profile to check my latest work and recent reviews. Let us make this great together, please connect in chat. Talk soon.

@alizain721

I’ve built low-latency, high-concurrency pipelines for CogVideo-like models that served hundreds of simultaneous sessions with sub-2s cold-start and stable steady-state latency. The real bottleneck isn’t just model size but memory-bound attention and inefficient batching. Fusing attention (xformers), TensorRT kernels, and smart dynamic batching usually yields the biggest ROI before aggressive quantization. I recently took a CogVideoX-based prototype into production for a content studio, converting key kernels to TensorRT, adding xformers attention, and using LoRA for consistent character renders. My approach: quick audit of your Wan2.2 / CogVideoX checkpoints, convert and profile with TensorRT/xformers, apply mixed-precision and calibrated quantization, run controlled LoRA fine-tuning with validation to prevent drift, then expose a Python/Node.js inference API with autoscaling and metrics. I keep hyperparameter sweeps small and targeted to save time. What target throughput, latency, and GPU types are you aiming for so I can size the pipeline correctly?

@ravd82

I’ve worked on AI pipelines where the main challenge was not just running models, but making them efficient, scalable, and reliable under real usage. I’m comfortable working with PyTorch based systems and building inference services that can handle high concurrency without performance drops. For your requirements, here is how I would contribute: - Set up and optimize inference pipelines for image and video generation, focusing on batching, memory management, and GPU utilization - Work with models like Diffusers and DiT based architectures, and adapt them for production use - Apply performance optimizations such as quantization, xformers, and TensorRT where applicable to reduce latency and cost - Implement LoRA fine tuning for style and character consistency, with attention to avoiding overfitting and maintaining generalization - Build clean API services for inference so the models are easy to integrate into your applications I also have experience moving from experimental setups to production ready systems, which usually involves restructuring pipelines, improving observability, and making deployments repeatable. If helpful, I can share examples of similar AI workflows I’ve worked on and how I approached performance optimization. Happy to discuss your current setup and where you are seeing the biggest bottlenecks. Best, Ravisher

@mugencodes

Hi there, Are you looking for an AI Image/Video Engineer who can design and optimize AI generation pipelines for high-concurrency inference? I have over 5 years of experience in AI/ML engineering, specifically in video and image generation systems. My strong PyTorch skills, coupled with hands-on experience in GPU inference optimization using TensorRT and quantization techniques, make me well-equipped to tackle your project. I am adept at integrating and fine-tuning open-source video generation models like Wan2.2 and CogVideoX. Additionally, my experience in LoRA-based fine-tuning for character consistency and style customization aligns perfectly with your requirements. I have collaborated with ML researchers in the past to streamline the transition from research to production. I am confident in my ability to independently build and deploy inference API services using Python and Node.js. Let's collaborate to optimize GPU inference performance and achieve your project goals seamlessly. Best regards, Jayabrata Bhaduri

@AZTURK1

Hi there, I will design and productionize high-concurrency AI image/video generation pipelines and implement LoRA fine-tuning for character consistency , my ML engineering and deployment background ensures research models (Wan2.2, CogVideoX, Diffusers) run reliably at scale with GPU optimizations. - Implement end-to-end inference service (Python or Node.js) with Docker, API endpoints, and monitoring for Wan2.2 or CogVideoX - Optimize GPU inference (TensorRT conversion, xformers integration, int8/4 quantization) and benchmark latency/throughput - Build LoRA fine-tuning workflows with hyperparameter sweeps, overfitting controls, and checkpoints for style/character consistency - Validation and rollback plan: staged deploy, load testing, automated performance regression checks, and fallback model paths Skills: ✅ PyTorch ✅ Diffusers ✅ LoRA fine-tuning workflow ✅ GPU inference (TensorRT, xformers) & deployment ✅ Quantization, benchmarking, reliability ✅ Node.js / Python inference API Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I’m available to start immediately. Which target throughput (requests/sec) and max latency SLO do you need for the inference service, and which GPUs are available for initial testing? Best regards,

@MIndlabsAi6

Hello, I am Vishal Maharaj, with 20 years of experience in Python, Node.js, AI Development, AI Model Integration, Generative Model, and AI Model Development. I have carefully reviewed your project requirements and propose to design and optimize AI image/video generation pipelines for high-concurrency inference. I will integrate and fine-tune open-source video generation models such as Wan2.2 and CogVideoX, optimize GPU inference performance using TensorRT, xformers, and quantization techniques, implement LoRA-based fine-tuning for character consistency and style customization, and collaborate with ML researchers for seamless research to production transition. I possess the necessary skills and experience to successfully execute this project. Please initiate a chat to discuss further. Cheers, Vishal Maharaj

@abdf2010

Hello. I came across your project, AI Image/Video Engineer and it aligns well with my background. I have hands-on experience with Python, Machine Learning (ML), Node.js that's directly relevant here. Feel free to reach out if you have questions.

@hr157

Hello, I’m an experienced AI/ML engineer with 7+ years in building and optimizing generative AI systems for production. I’ve worked extensively with PyTorch, Diffusers, and DiT-based architectures, and have hands-on experience deploying high-concurrency inference pipelines. I’ve optimized GPU performance using TensorRT, xformers, and quantization, and implemented LoRA fine-tuning for style and character consistency. I can design scalable APIs (Python/Node.js) and efficiently bridge research to production. I’d be happy to share relevant work and discuss how I can accelerate your pipeline.

@dmytroy7

Hi, there. I read your project and I understand that the main challenge here is designing and optimizing AI image and video generation pipelines for high concurrency. I have worked on similar projects before and I know how to solve this properly. For this, I will use Python, PyTorch, TensorRT, and other related tools. My approach starts with detailed analysis of your existing pipeline, ensuring seamless integration and optimization techniques to maximize GPU performance. This way, the final result fits exactly what you need. I have strong reviews on Freelancer and clients from different countries trust my work and come back. Could you clarify if you have any specific requirements for the character customization workflow? Let's talk - reply and we can discuss the details. Dmytro

@AlexDeveloper911

Hey, I can start now. ✅ I’ve worked on something very similar. What really matters here is optimizing GPU inference performance and integrating open-source video generation models. Most projects fall apart when scaling high-concurrency inference pipelines. I recently worked on optimizing GPU performance using TensorRT and implementing fine-tuning techniques for consistency. While I haven't specifically worked with LoRA-based fine-tuning, I have experience with similar character customization methods. Let's chat more details. -Alex