
Staff / Senior AI Engineer (Video AI & LLM Systems)
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About Lucidya
Lucidya is an AI-native platform for customer experience (CX) intelligence that manages entire customer lifecycles autonomously, from initial engagement through retention and growth. Unlike platforms that only surface insights and leave the action to you, Lucidya completes the loop with proprietary NLU capabilities developed entirely in-house and trained on millions of multilingual conversations, equipping marketing, support, CX, and research teams to deliver personalized experiences that drive measurable improvements in customer satisfaction, retention, and lifetime value.
Why This Role Matters
We’re expanding our CXM platform into video intelligence and multimodal AI.
Today, we already analyze massive volumes of social and enterprise data. Tomorrow, we’ll understand video - sentiment, context, intent - across visual, audio, and text signals. This isn’t a side experiment. It’s a core product direction.
Right now, our AI team has strong mid-level and junior engineers. What we’re missing is a senior builder - someone who can walk into ambiguity, define the path forward, and ship production-grade AI systems without waiting to be told what to do.
This role matters because:
• You’ll own video AI from concept to production.
• You’ll shape how we deploy and optimize self-hosted LLMs.
• You’ll influence architecture and engineering standards.
• You’ll directly impact what our enterprise customers experience.If you like clear tickets and step-by-step instructions, this won’t be comfortable.
If you like ownership, autonomy, and building things that go live - you’ll thrive.
What You’ll Do
You’ll Own Outcomes, Not Just Tasks
You won’t be handed isolated tickets. You’ll partner with Product to define a roadmap and then execute against it.
You’ll take ideas like “video listening” and turn them into deployed, reliable features that customers use.
That means:
• Designing and building end-to-end video analysis pipelines (visual + audio + text).
• Extracting sentiment, intent, and semantic meaning from multimodal data.
• Deploying models into secure, production environments.
• Making sure what you ship doesn’t just work in a notebook, but survives in production.You’ll Build and Run Self-Hosted LLM Systems
Saudi data regulations require us to host models privately. You’ll:
• Own and optimize self-hosted LLM infrastructure.
• Manage inference pipelines and performance trade-offs.
• Ensure scalability, reliability, and security.
• Think beyond model accuracy, into cost, latency, and uptime.You’re not just a researcher. You’re an end-to-end AI builder.
You’ll Raise the Bar for the Team
You won’t have direct reports, but you will:
• Mentor mid-level and junior engineers.
• Review code and influence architecture.
• Bring clarity to technical discussions.
• Push for higher standards when needed.We value people who can disagree respectfully and explain their reasoning clearly.
In Your First 90 Days
You’ll:
• Deep dive into our existing video capabilities.
• Identify gaps and opportunities.
• Define a clear roadmap with Product.
• Start delivering meaningful improvements.We measure success by what gets deployed, not by how many experiments you ran.
Requirements
Who You Are
You have 5+ years (Senior) or 7-8+ years (Staff) in Machine Learning, with strong depth in:
• Computer Vision
• Video Analysis
• Large Language Models
• Multimodal systemsYou’ve shipped real systems to production, not just prototypes.
You understand what it means to own the full lifecycle: modeling, infrastructure, deployment, monitoring.
You’re Comfortable Hosting Models Yourself
You’ve:
• Deployed models in containerized environments (Docker, Kubernetes).
• Worked with cloud or on-prem infrastructure.
• Managed performance, scaling, and reliability.
• Hosted LLMs or large models in controlled environments.If your experience stops at training models and handing them off, this role won’t be a fit.
You’re Proactive (Truly)
When you see ambiguity, you:
• Propose a direction.
• Validate assumptions.
• Move forward.
• Communicate clearly.You don’t sit on the fence, but you also don’t go rogue. You align, then execute.
You Communicate with Extreme Clarity
We care deeply about clarity here.
You Thrive in Fast-Moving Environments
Lucidya is fast-paced and dynamic.
There are many moving parts.
You don’t get frustrated by that, you bring structure into it.
Nice to Have (Not Required)
• Experience in B2B SaaS or CX platforms.
• Experience at a recognizable technology brand.
• Arabic multimodal or NLP exposure.
• OCR for Arabic text in video.
• Generative video or diffusion models.
• Edge AI optimization.
What the Hiring Process Will Look Like
• Screening Call with a TA Lead
• First Interview - with our AI team
Focus: technical depth + real-world experience + impact
• Second Interview with a Live Case Study - with the AI Director
You’ll walk through a real scenario (no take-home assignments).
Expect deep technical probing and discussion.We value clarity and depth over theatrics.
If you’re looking for ownership, autonomy, and the chance to shape how video AI and self-hosted LLMs are built in the region… this is that role.
We’re building seriously.
Requirements
- •Senior engineer to build production-grade AI systems
- •Design and build end-to-end video analysis pipelines (visual + audio + text)
- •Deploy models into secure, production environments
- •Own and optimize self-hosted LLM infrastructure
- •Manage inference pipelines and performance trade-offs
- •Ensure scalability, reliability, and security of AI systems
- •Experience in multimodal AI and large language models
- •Ability to work with ambiguity and define the path forward
Nice to Have
- •Experience with Saudi data regulations
- •Understanding of cost, latency, and uptime in AI systems
Responsibilities
- •Own video AI from concept to production
- •Shape deployment and optimization of self-hosted LLMs
- •Influence architecture and engineering standards
- •Partner with Product to define roadmap and execute
- •Turn ideas into deployed, reliable features
- •Mentor mid-level and junior engineers
- •Review code and influence architecture
- •Raise standards for the team
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