Principal AI Engineer
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Key skills for this role
About the Role
Smart Connected Solutions seeks a Principal AI Engineer in Dubai to architect and implement production AI applications and agentic workflows. The role involves enterprise integrations, data pipelines, MLOps, and technical direction.
Key Skills for This Role
Responsibilities
- Architect and implement production AI applications and agentic workflows, owning each one from concept to running system
- Connect AI systems to enterprise platforms, APIs, and external services through integration layers
- Turn messy, real world unstructured data into production grade extraction and processing pipelines
- Stand up and run deployment, CI/CD, and monitoring infrastructure behind AI systems
- Define architecture patterns, evaluation strategy, and guardrails for how we build AI, and prove them out in code
- Drive observability, evaluation, latency, and cost across AI workloads
Requirements
- Strong backend engineering background with production Python
- Track record of carrying AI systems solo across full lifecycle — design, implementation, deployment, and operation
- Experience building agentic / LLM based systems with clear point of view on system design and reliability
- Experience integrating AI with enterprise systems and APIs
- Practical RAG and document intelligence experience
- Solid MLOps and DevOps foundation: CI/CD, containerization, deployment, monitoring, and infrastructure
- Sound judgment on production LLM concerns: evaluation, latency, cost, and scaling
- Ability to operate as a technical leader — owning ambiguous problems end to end and setting direction for others
Full Job Posting
About the Role
- We're looking for a Principal AI Engineer who owns AI systems end to end — from the architecture on the whiteboard to the code in production.
- You'll work across a wide range of enterprise problems, combining backend engineering depth with applied LLM expertise.
What You'll Be Working On
- AI systems, end to end: Architect and implement production AI applications and agentic workflows
- Enterprise integrations: Connect AI systems to enterprise platforms, APIs, and external services
- Data and document pipelines: Turn messy, real world unstructured data into production grade extraction and processing pipelines
- MLOps and DevOps: Stand up and run the deployment, CI/CD, and monitoring infrastructure
- Technical direction: Define the architecture patterns, evaluation strategy, and guardrails
- Production quality: Drive observability, evaluation, latency, and cost across AI workloads
What We're Looking For
- Strong backend engineering background with production Python
- Track record of carrying AI systems solo across the full lifecycle
- Experience building agentic / LLM based systems
- Experience integrating AI with enterprise systems and APIs
- Practical RAG and document intelligence experience
- Solid MLOps and DevOps foundation: CI/CD, containerization, deployment, monitoring, and infrastructure
- Sound judgment on production LLM concerns: evaluation, latency, cost, and scaling
- Ability to operate as a technical leader
Nice to Have
- Experience delivering multi tenant SaaS or enterprise grade solutions
- Computer vision or multilingual NLP experience
- Familiarity with modern cloud and deployment stacks
- Computer vision experience (e.g., layout/document models, YOLO family)
- Comfort across the deployment stack (Render, containerized deploys, CI/CD)
- Mobile app development experience
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