AI Engineer

• Responsible for the overall architecture of the organization’s AI solutions within the MS Azure environment
• Create architecture diagrams
• Maintain architecture documentation
• Guide and support the development team
• Advise on the use of Azure AI Foundry features
• Present solutions and design decisions in governance and architecture forums

Lead Azure AI Architect

Machine Learning Engineer

• Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies.
• Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse).
• Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints.
• Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support.
• ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling.
• Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems.
• Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers.
• Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role.

Senior ML Engineer – Offline Team

• Develop, train, and optimize machine learning models for various mobile app features.
• Research and implement state-of-the-art AI techniques to improve user engagement and app performance.
• Collaborate with cross-functional teams to integrate AI-driven solutions into our applications.
• Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring.
• Analyze large datasets to derive insights and drive data-driven decision-making.
• Stay updated with the latest AI trends and best practices, incorporating them into our development processes.
• Optimize AI models for mobile environments to ensure high performance and low latency.

AI/ML Engineer

LLM Engineer

• Inference pipelines: Implement modular inference pipelines using SOTA Latent Diffusion Models to facilitate complex image-to-image pipelines.
• Advanced Inpainting & Editing: Develop robust algorithmic solutions for targeted inpainting, outpainting, and coherent background generation within existing scenes.
• Semantic Processing: Integrate SOTA segmentation models to automatically identify and isolate specific image regions for targeted visual modification.
• Complex Chaining: Utilize custom Python backends to chain non-linear workflows, combining detection, segmentation, and generation steps.
• Geometric & Structural Conditioning: Implement and fine-tune structural conditioning preprocessors to ensure generated elements strictly respect the vanishing points, perspective, and geometry of the source input.
• Style Adaptation: Train and maintain parameter-efficient fine-tuning (PEFT) adapters to steer models toward specific high-value aesthetic styles and lighting conditions without catastrophic forgetting.
• Automated Quality Scoring: Develop AI-on-AI scoring systems using advanced computer vision metrics to detect hallucinations, artifacts, or perspective failures before human review.

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Lead Azure AI Architect

Senior ML Engineer – Offline Team

AI/ML Engineer

Generative AI Engineer