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# Generative AI Pipeline Engineer (Tech Lead)

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/capslock.md)Share30 Jul 2026Published31Listing views3Application actionsManually reviewedTrust & Safety status  Opportunity details

## About this role.

AI SummaryCapsLock is seeking a Tech Lead for their AI Media Production platform, responsible for architecting and running generative pipelines for image and video generation. The role involves building control-first, reproducible pipelines using ComfyUI or similar technologies, training LoRAs, and integrating QC automation. The ideal candidate has production-grade experience with generative pipelines, strong Python skills, and expertise in control techniques like ControlNet and IP-Adapter. This is a greenfield project, offering high autonomy and direct collaboration with the Design Director. The position is fully remote with benefits including paid vacations and learning opportunities.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightThis role demands deep technical expertise in generative AI, pipeline architecture, and control techniques, combined with leadership responsibilities, making it one of the most challenging roles in the AI engineering field.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate$180,000US market range$130k–$250k0$275k

AI insightThe offered salary is not disclosed, but based on market benchmarks for a Tech Lead in Generative AI, the median is around $180,000. This is a competitive range for the specialized skill set required, especially considering the remote nature and benefits package.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Generative AI](https://jobicy.com/jobs?search_keywords=Generative%20AI.md)[ComfyUI](https://jobicy.com/jobs?search_keywords=ComfyUI.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[LoRA Training](https://jobicy.com/jobs?search_keywords=LoRA%20Training.md)[ControlNet](https://jobicy.com/jobs?search_keywords=ControlNet.md)[IP-Adapter](https://jobicy.com/jobs?search_keywords=IP-Adapter.md)[Pipeline Architecture](https://jobicy.com/jobs?search_keywords=Pipeline%20Architecture.md)[AI Media Production](https://jobicy.com/jobs?search_keywords=AI%20Media%20Production.md)[Tech Lead](https://jobicy.com/jobs?search_keywords=Tech%20Lead.md)[Remote Work](https://jobicy.com/jobs?search_keywords=Remote%20Work.md)

Cover letter sampleDear Hiring Manager,

I am excited to apply for the Generative AI Pipeline Engineer (Tech Lead) role at CapsLock. With extensive experience in building production-grade generative pipelines using ComfyUI and diffusers, I have a proven track record of deploying modular, reproducible systems that prioritize control and accuracy. My background includes end-to-end LoRA training and integrating advanced techniques like ControlNet and IP-Adapter to ensure high-fidelity outputs.

I thrive in greenfield environments and am passionate about creating tools that empower non-technical teams to generate assets that match real products perfectly. At my previous role, I led the development of an internal AI platform that reduced turnaround time by 40% while maintaining strict quality standards. I am eager to bring this expertise to CapsLock and help build the AI Media Production platform from the ground up.

Thank you for considering my application. I look forward to discussing how I can contribute to your team.

Sincerely,
[Your Name]

Copy   Sample interview questionsCan you describe a time you built a production-grade generative pipeline from scratch? What were the key challenges and how did you ensure reproducibility?I led the development of an image generation pipeline for a fashion e-commerce client. The main challenge was ensuring that generated images matched product details exactly. We used ComfyUI with custom nodes for control, versioned workflows with Git, and automated QC checks using image similarity metrics. The pipeline was adopted by the creative team and reduced manual touch-ups by 30%.

How do you approach training a LoRA model from a dataset of real product photos? Describe your process from data curation to evaluation.

First, I clean and preprocess the dataset, ensuring consistent lighting and background removal. I use captioning models to generate annotations. During training, I monitor loss and evaluate on a held-out set using FID and CLIP score. I iterate on hyperparameters and sometimes use data augmentation. Finally, I test the LoRA on unseen products to verify it generalizes without overfitting.

Explain how you would integrate ControlNet into a pipeline for video generation. What are the trade-offs?

For video, I would apply ControlNet per frame with temporal smoothing to avoid flickering. Key trade-offs include increased computational cost and potential loss of temporal coherence if conditioning is too strong. I'd use a lightweight ControlNet variant and post-process with flow-based smoothing. The goal is to maintain product fidelity while ensuring natural motion.

As a tech lead, how would you balance technical debt with shipping features in a greenfield project?

I'd prioritize building a modular architecture from the start to minimize debt. For each feature, I evaluate the cost of delaying vs. the impact of imperfect implementation. I allocate 20% of sprint capacity for refactoring and encourage the team to document known issues. Communication with the design director helps align on pragmatic trade-offs.

How would you design a one-click UI for non-technical users to run complex generative pipelines?

I'd create a web interface with pre-configured presets for common use cases (e.g., product shots, lifestyle images). Under the hood, it sends requests to a backend running ComfyUI with parameter validation. The UI provides sliders for key controls (e.g., strength, style) and real-time previews. Error messages are user-friendly, and advanced settings are hidden by default.

## Description

CapsLock is a global IT marketing company that pioneers unique, scalable customer acquisition solutions for B2C clients. We solve complex sales challenges across a wide range of industries, primarily for large North American partners. By integrating our expertise in Digital Marketing, IT, Design, AI-driven analytics, proprietary MarTech, and sales consulting, we deliver powerful, data-informed customer acquisition solutions.

At CapsLock, people, technology, and forward-thinking innovation lie at the heart of everything we do. Our diverse, global team, fluent in over 10 languages, thrives on open collaboration and the exchange of new ideas, pushing boundaries to ensure our clients’ success.

Role overview

We are building CapsLock’s AI Media Production platform, and we’re hiring the tech lead who will architect it.

We generate visuals of real products, not concepts. Every asset must match the client’s actual product 1:1 – down to hardware and geometry. No artifacts, no hallucinations, no “close enough”. This is a control and reproducibility problem, not a prompting problem, and it needs an engineer.

This is a greenfield build. You’ll be the first engineer in this direction, working directly with the Design Director as the technical interface between creative teams and software infrastructure.

What you will work on

* Architect and run our AI Media Production platform: serving ComfyUI or similar engines, GPU environments, one-click UIs for non-technical teams;
* Ship modular, versioned, reproducible generative pipelines, node-based and/or code-first (e.g. diffusers);
* Develop one-click presets and use cases for image and video generation;
* Train LoRAs end-to-end: from real product photography to evaluated, production-ready models;
* Build control-first generation into every pipeline: ControlNet, IP-Adapter, inpainting, reference conditioning;
* Engineer QC into the pipeline itself: automated checks against product references, before a human ever reviews;
* Build our creative intelligence loop: encode what makes our best-performing assets work into reusable presets and styles;
* Own the technical roadmap with the Design Director; qualify new models and tools; drive adoption through enablement, not mandates.

## Requirements

Your must haves

You don’t need to meet every single requirement. If you’re excellent in most of these areas and can show shipped work, we’d love to hear from you.

* Shipped generative pipelines adopted and used daily by other people;
* Production-grade workflows in ComfyUI or similar technologies, or equivalent code-first experience (e.g. diffusers);
* Strong Python: automation, API integrations, custom nodes;
* LoRA training end-to-end, from dataset curation to evaluation;
* Control techniques across modalities (t2i, i2i, i2v, v2v): ControlNet, IP-Adapter, inpainting, reference conditioning;
* Deploying pipelines as internal tools;
* Enough visual literacy to set the quality bar (no design portfolio required);
* Confident English (B2+).

Nice to have

* 3D/render-hybrid workflows: Blender or CAD renders as geometric ground truth with generated environments;
* LLM orchestration and agentic workflows: automated prompt generation, VLM-based QC;
* Experience producing visuals for performance marketing.

What we offer

* Remote Work – we offer a truly and fully remote environment. You choose where you are the most productive and comfortable to have an impact.
* Paid vacations – generous paid vacation policy to ensure you have time to recharge
* Unlimited Sick Days – we understand that being only human means getting sick or feeling under the weather from time to time, so we guarantee you time off as long as you need to recover and get back on your feet.
* Ongoing Learning – people at CapsLock are deeply inquisitive and eager to learn new knowledge and skills, that’s why we support and create learning opportunities like the Free Books program, workshops, conferences, and more.
* Home Office – we will cover the equipment and furniture expenses to make sure you have the best work-from-home experience.
* Physical Well–Being – We will cover costs of your medical insurance. Additionally we offer an allowance for a flexible fitness program.
* Fun Stuff – there is never a shortage of fun stuff

Show more

[Apply now >](https://jobicy.com/jobs/149857-generative-ai-pipeline-engineer-tech-lead.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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