Senior AI Engineer for Production Media Pipeline

@Julian29

Hello, I have shipped multiple production-grade AI media pipelines and am comfortable working in an execution-only, time-boxed environment. I bring strong experience with Python (FastAPI/async) backends, Dockerized GPU services, Redis queues/locking, and S3-compatible storage, with hands-on delivery of SDXL + ControlNet pipelines using identity-conditioning methods such as IP-Adapter / InstantID in live systems. I’ve stabilized inference pipelines under real user load, implemented deterministic execution, identity locking, idempotent job orchestration, retries, and GPU worker lifecycle management on RunPod and cloud GPU environments. I understand the requirement for no R&D, no experimentation, no model training, and I am confident delivering within a strict 4-week window based on predefined specifications. I can clearly scope, execute, and harden what is realistically achievable in that timeframe. I’m available to start immediately and commit near full-time. Thanks.

@corpmember29

I HAVE SUCCESSFULLY DELIVERED AND HARDENED PRODUCTION-GRADE SDXL + CONTROLNET AI PIPELINES UNDER REAL USER LOAD — THIS PROJECT IS A PERFECT MATCH. I am a senior backend AI engineer with hands-on experience executing and stabilizing production AI media pipelines with strict delivery timelines, deterministic outputs, and zero R&D scope. I focus on execution, reliability, and delivery, exactly as required. Core Execution Scope • Harden existing Python (FastAPI/async) backend • Stabilize SDXL + ControlNet + identity-conditioning pipeline • Implement strict identity locking with downstream reuse • Ensure deterministic, repeatable inference • Redis-based job queues, locking, retries, and idempotency • Dockerized services with GPU worker lifecycle management • Cost-aware GPU execution on RunPod / AWS / GCP • Production-grade failure handling and monitoring hooks User Roles • Admin – system configuration, GPU control, monitoring • Backend Service – owns all AI execution and orchestration • Worker Nodes – isolated, deterministic GPU inference execution Delivery Commitment • Execution-only (no R&D, no experimentation) • Strict 4-week delivery • Complete source code ownership • 2 YEARS FREE ONGOING SUPPORT post-launch (bug fixes & stability)

@technosystem

With numerous AI projects under my belt, including Python backend systems, containerization with Docker, and working with Redis for jobs, state management, and locking, I am confident that I can execute the development and stabilization of your existing AI media pipeline without a hitch. Being well-versed in executing production-grade identity locking and ensuring deterministic outputs, I can guarantee secure and consistent performance in your application. Moreover, throughout my extensive 19+ years of experience, I have had considerable exposure to GPU inference in production setting which aligns with your requirement of running AI pipelines on RunPod or AWS GPU. The way you emphasize on strict adherence to timelines drove me to highlight my reliable and on-schedule deliveries complemented by my strong communication skills. This is crucial as it guarantees we remain aligned throughout the execution timeframe for successful project culmination. Lastly, while I bring the desired expertise with SDXL/ControlNet/identity conditioning coupled with the ability to clearly prioritize tasks for efficient delivery within four weeks. But apart from being an architectural person deeply ingrained in software solutions for various industries for many years now, what sets me apart from other candidates is my commitment to offering real business growth solutions through innovative thinking and constantly challenging myself to learn new technologies.

@neerajdhameliya

Hello, I’m an experienced backend AI engineer with a proven track record of shipping production-ready AI systems. I can execute and stabilize your media pipeline within the 4-week delivery window, focusing strictly on execution, reliability, and delivery. Experience & Skills: => Shipped SDXL + ControlNet pipelines for live apps, including identity-conditioning (IP-Adapter / InstantID) with deterministic outputs. => Managed GPU inference on RunPod and AWS, including worker lifecycle, cost optimization, and environment tuning. => Developed Python backends (FastAPI / async), Dockerized services, and Redis-based queues/state/locking, hardened for production. => Implemented async job orchestration, retry logic, idempotency, and persistent job state. I can stabilize your pipeline, enforce single-identity locking, guarantee deterministic outputs, and deliver production-ready services on schedule. I adhere strictly to execution-only work and can commit full-time for 4 weeks. I’d be happy to share examples of pipelines I’ve shipped and discuss delivery in chat. Best regards, Niral

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Software Architecture, Machine Learning (ML), Redis, Git, Docker, Backend Development, Containerization, FastAPI, AI Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@devendrathakur12

Hello, I specialize in production AI backends and I’ve built and customized large-scale media pipelines that run under real user load. The main challenge here is making an existing AI system fully stable, repeatable, and safe to run every day without surprises. I’m certified in Python backend development and I will solve this by hardening the current FastAPI pipeline with Docker, Redis queues, GPU workers, and strict identity locking for SDXL and ControlNet. No experiments, just clean execution and delivery in 4 weeks. I’ve shipped systems where reliability mattered more than new ideas. A few questions for you. Is identity locking already partially implemented? How do you validate deterministic output today? What happens when a GPU job fails mid-run? Do workers scale automatically? Are logs centralized for quick debugging? This will reduce failures and keep costs under control. Best regards, Dev S.

@rbtech1984

Hello There!!! ⚜⭐⭐⭐⭐⚜(( Stabilise and harden a production ready AI media pipeline in four weeks ))⚜⭐⭐⭐⭐⚜ This role is clearly about execution under constraints, not experimentation. You already have a defined AI media pipeline and need someone who can take what exists, remove instability, and deliver a deterministic, production safe system within a strict four week window. The focus is on backend owned AI execution using Python async services, Redis based orchestration, and GPU workers running SDXL with ControlNet and identity conditioning. Identity locking, repeatable outputs, and failure handling are non negotiable, which signals a real consumer scale workload rather than a prototype. I have worked on production AI backends where stability, idempotent jobs, and GPU cost control mattered more than model novelty. My approach would be to follow the supplied specifications closely, harden queues and locks, enforce deterministic execution paths, and stabilise worker lifecycles without scope creep. Three critical delivery pillars: Deterministic SDXL pipeline with identity locking Resilient async orchestration with retries and persistence Production hardened GPU execution with predictable costs If you would like, we can align quickly on the existing system state and confirm what will be fully delivered within the four week window. Warm Regards, Farhin B.

@agilifytechllc

Hi Albaspec, Your project vision is genuinely impressive, moving from a partial AI setup to a hardened, production-ready pipeline with a strict focus on identity locking and deterministic outputs shows you understand exactly what it takes to scale consumer AI. To stabilize, dockerize, and harden an existing SDXL identity-locking pipeline for reliable, deterministic production use. Core Strategy: --> Identity Integrity: Implementing production-grade identity locking using IP-Adapter or InstantID to ensure repeatable user results. --> Reliable Orchestration: Hardening the FastAPI + Redis backend for robust job queuing, retries, and state management. --> GPU Ops & Scaling: Tuning Dockerized workers on RunPod for cost-efficient and high-performance inference. --> Zero-Experimentation Delivery: A 100% execution-focused approach to meet the strict 4-week deployment deadline. A few quick questions: Since we are focusing on identity locking, do you have a preferred reference image pre-processing logic already in place, or should we strictly follow the provided specifications? Are the Redis queues currently suffering from specific bottleneck issues (like race conditions or memory leaks) that we should prioritize in the first week? I will focus on your requirements. I worked on similar projects and can share upon request. Let’s discuss timeline and budget based on these details via chat. Regards, Atta

@softwarelinkerrs

With over a decade of experience in backend development and Python, I am confident in my skills to execute your project to the highest standards. I have a deep understanding of Dockerized services, Redis, and FastAPI or equivalent asynchronous frameworks that will be valuable for hardening and stabilizing your current backend AI system. My experience with running AI pipelines on various platforms like RunPod and AWS GPU further ensures that I am fully capable of handling the orchestration and reliability aspects of this project. Although the engagement is strictly time-boxed, my team at SoftwareLinkers has a proven track record of delivering complex projects within demanding deadlines without compromising quality. In addition to technical prowess, we also bring a holistic approach to our work, aligning our solutions with your long-term goals while maintaining clear communication throughout the entire process. While execution and reliability is key for this project, we strongly believe in future-ready solutions. Hence our optional experience with AI-powered features & system integrations can add value to your requirements even within this execution-only mandate. Being world-facing professionals guarantee we have the adaptability necessary for fast-paced delivery window like yours. Contact us today so we can discuss how we can transform your existing almost-there backend AI system into a high-performing production pipeline in just 4 weeks!

@itswahabahmad

As an experienced AI engineer, this project is in perfect alignment with my skillset. I have a proven track record of shipping production AI systems, which includes extensive experience with Python backend systems such as FastAPI and Redis, along with my proficiency in Docker and containerized services that are essential for ensuring a robust media pipeline. Not only am I comfortable with Linux-based GPU inference, but I am also adept at runtime environment optimization and production hardening, skills which can prove invaluable when dealing with high-demand applications. Another area where my experience aligns deeply with your project requirements is my familiarity with SDXL alongside ControlNet and identity conditioning (IP-Adapter, InstantID, or equivalent). Managing and stabilizing these pipelines under real user load is a task that I have accomplished successfully during my tenure. Moreover, I understand the importance of deterministic execution and identity locking and can ensure their effective implementation within the determined time frame.

@Solutionzhere

Hey, We’ve shipped prod AI media pipelines for global apps (ex-TCS/Deloitte), hardening FastAPI async backends with Docker/Redis queues, RunPod GPU workers, and SDXL+ControlNet for deterministic gen under load. Nailed identity locking via IP-Adapter reuse for repeatable outputs. Pure execution pros. 4 weeks fits our track record for stabilization. Propose 85 CAD/hr (160 hrs full-time). Quick Q: RunPod GPU type (A100/RTX?) and expected concurrency? Confirm: Execution-only ✓, No R&D ✓, 4-week delivery ✓. Examples: Stabilized SDXL media gen for e-comm (portfolio on request), ControlNet identity cond in live apps. Ready to lock & deploy! — Team SolutionzHere

@EngrAnasAwan5

As a seasoned AI and machine learning engineer with a wealth of experience in building and deploying production-grade AI systems, I am uniquely suited to tackle your project. I completely understand the need for reliability, execution, and delivery - qualities that I have always prioritized in my work. My Python backend skills including FastAPI as well as experience with Docker and Redis align perfectly with your requirements for backend and infrastructure optimization. Furthermore, my expertise in GPU inference execution, orchestration, and reliability ensures that I have the acumen to not just stabilize your existing SDXL pipeline but to do it right under real user load. In addition to these skills, I also bring practical experience in identity locking, deterministic execution, cost-aware inference execution, and more - all areas that you’ve clearly underscored in your project description. Lastly, my long-term vision encompasses ensuring your system does not solely meet its current needs but is scalable for expanding consumer demands. I am prepared to dedicate my time fully to meeting your project timeline because I know how critical it is to respect such constraints. My commitment to delivering quality work on-time and on-budget paired with the reputation I have maintained as a trusted freelancer makes me confident that I can offer unique value

@Azursol

Hello Albaspec! I’m excited about the opportunity to help with your project. Based on your requirements, I believe my expertise in Python aligns perfectly with your needs. How I Will Build It: I will approach your project with a structured, goal-oriented method. Using my experience in Python, Software Architecture, Machine Learning (ML), Redis, Git, Docker, Backend Development, Containerization, FastAPI, AI Development, I’ll deliver a solution that not only meets your expectations but is also scalable, efficient, and cleanly coded. I ensure seamless integration, full responsiveness, and a strong focus on performance and user experience. Why Choose Me: - 10 years of experience delivering high-quality web and software projects - Deep understanding of Python and related technologies - Strong communication and collaboration skills - A proven track record — check out my freelancer portfolio. - I’m available for a call to discuss your project in more detail - Committed to delivering results on time, every time Availability: I can start immediately and complete this task within the expected timeframe. Looking forward to working with you, Albaspec! Best regards, Ali Zahid Canada

@mughiraaa

Hello, I have reviewed the details of your project. i will audit the existing python backend and dockerized ai services to map the current sdxl + controlnet + identity-conditioning pipeline. i will stabilize the async fastapi endpoints, redis job queues, and gpu worker lifecycle to ensure all tasks are deterministic and repeatable. identity locking will be enforced using redis-based locks to guarantee a single identity is reused across the downstream pipeline. job orchestration will include retry logic, idempotency checks, and proper error handling to prevent dropped or duplicated tasks. all s3-compatible storage interactions will be validated and hardened for concurrency, and resource usage will be monitored to keep inference costs predictable on runpod gpus. Let's have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@mobiwebsolutions

Hi There!!! THE PROJECT GOAL:- Stabilize and productionize an existing AI media pipeline with SDXL and ControlNet, ensuring deterministic execution, identity locking, and full reliability within a 4-week timeframe. I have carefully read and understood your project description and am confident I can deliver a fully production-ready backend AI system that meets your strict execution-only requirements. I am the best fit because I have hands-on experience shipping production AI pipelines with SDXL and ControlNet, managing GPU workloads, and building robust Python backend systems. 1. Build and stabilize Python backend pipelines using FastAPI with Dockerized services 2. Implement Redis-based job queues, state management, and identity locking 3. Manage GPU worker lifecycle for production AI inference on cloud platforms I provide database management, testing, and full source code delivery at project completion. I have 9+ years experience as a full stack developer and have successfully delivered production AI pipelines for media generation systems with deterministic and repeatable outputs. Looking forward to chat with you for make a deal Best Regards Elisha Mariam!

@sekai1116

I’m a senior backend AI engineer with extensive experience shipping production AI systems. I can execute, stabilize, and harden your existing SDXL + ControlNet + identity-conditioning media pipeline within your strict 4-week execution-only timeline. Relevant experience: Production AI backends: Python (FastAPI), async pipelines, Dockerized services Job orchestration: Redis queues, state management, locking, GPU worker lifecycle Media pipelines: SDXL, ControlNet, identity conditioning (IP-Adapter/InstantID), deterministic execution Cloud GPU deployment: RunPod, AWS, GCP Production hardening: retry logic, idempotency, and stable inference at scale I have shipped multiple GPU-powered production AI pipelines with strict determinism and identity locking, fully containerized and orchestrated with async job management. I am comfortable with the execution-only scope, no R&D, and will deliver reliably within 4 weeks. Best regards, Jiayin

@techsty2014

Dear Client, I am excited about your project "Senior AI Engineer for Production Media Pipeline" and confident I can deliver excellent results. With strong experience in similar work, I understand your requirements and can start immediately. I would love to discuss your project further and answer any questions. Thanks and best regards, Faizan

@codefusion53

Hello. I am a senior backend AI engineer with extensive experience shipping production AI systems, including SDXL and ControlNet pipelines with identity-conditioning. I have stabilized AI media pipelines under real user load and implemented deterministic, repeatable outputs in production. I am fully comfortable working within an execution-only scope with a strict 4-week delivery timeline. My expertise includes Python backend systems (FastAPI/async), Dockerized services, Redis queues and locking, and GPU-based inference on cloud platforms like RunPod, AWS, and GCP. I have hands-on experience consolidating SDXL + ControlNet pipelines and implementing production-grade identity locking for downstream tasks. I am skilled at async job orchestration, retry logic, failure handling, and GPU worker lifecycle management. I can ensure cost-aware inference execution and pipeline hardening to deliver a stable, production-ready system. I am confident in delivering the predefined specifications on schedule while maintaining deterministic outputs and high reliability. Regards, Justin.

@SubhanaWaseem

Dear Hiring Team, I am a seasoned Full Stack Engineer with a strong background in Web and Mobile Application Development, boasting over 8 years of experience in the industry. I have successfully delivered numerous projects, showcasing my expertise in both front-end and back-end technologies. My proficiency includes but is not limited to HTML, CSS, JavaScript, React, Node.js, and MongoDB. I have a proven track record of developing scalable and efficient applications that meet clients' requirements and exceed their expectations. With a keen eye for detail and a passion for problem-solving, I am confident in my ability to contribute effectively to your project. I am excited about the opportunity to collaborate with you and bring my skills to the table. I am committed to delivering high-quality work and ensuring the success of your project. Thank you for considering my proposal, and I look forward to the possibility of working together.