Senior AI Systems Engineer

Role Overview We are looking for a Senior AI Systems Engineer who can translate research concepts and product requirements into reliable, production-grade AI systems. You will own the design, implementation, optimisation, evaluation, and deployment of Cliply’s AI processing pipelines. You will work closely with the company’s product and technical leadership to build reusable AI components for understanding video, audio, and text understanding. This is a hands-on engineering role. You will be expected to write code, run experiments, evaluate models, design system components, deploy services, and resolve production issues. Key Responsibilities Multimodal AI Development Design, implement, and optimise AI pipelines that process video, audio, speech, images, and text. Integrate pretrained and open-source models for computer vision, video understanding, speech processing, language understanding, and multimodal reasoning. Develop approaches for combining signals across modalities, including visual content, dialogue, audio events, metadata, and temporal context. Build reusable AI abstractions that can support multiple downstream product applications. Video and Temporal Intelligence Develop systems for frame, shot, scene, event, and segment-level video understanding. Work with sequential and temporal information to identify actions, transitions, relationships, and meaningful moments. Design appropriate frame-sampling, clip-sampling, windowing, aggregation, and temporal-fusion strategies. Evaluate approaches for long-form video processing while balancing quality, latency, and compute cost. Model Evaluation and Experimentation Evaluate and benchmark models using appropriate datasets, baselines, and quality metrics. Design reproducible experiments and perform error analysis, ablation studies, and model comparisons. Identify model limitations, hallucinations, failure modes, data-quality issues, and domain-specific gaps. Develop clear evaluation frameworks for AI-generated metadata, classifications, rankings, and clips. Production AI Engineering Build, test, deploy, and maintain production AI services and inference pipelines. Develop scalable APIs and asynchronous processing workflows for large media files. Take ownership of performance, reliability, maintainability, observability, and failure recovery. Optimise model serving for latency, throughput, memory utilisation, and infrastructure cost. Implement versioning, monitoring, logging, and reproducibility across models, datasets, prompts, and configurations. Model Optimisation and Deployment Convert and optimise models using appropriate technologies such as ONNX Runtime, TensorRT, quantisation, pruning, batching, or distillation. Deploy AI workloads across cloud, GPU, and containerised environments. Profile inference pipelines and resolve bottlenecks across preprocessing, inference, post-processing, data movement, and storage. Evaluate when to use self-hosted models, managed APIs, or hybrid inference architectures. Architecture and Collaboration Work with the Lead Architect and product leadership to translate proprietary concepts into implementable technical designs. Contribute to data models, schemas, APIs, storage strategies, and system interfaces. Participate in technical design reviews and challenge assumptions constructively. Document architecture decisions, model behaviour, experiments, interfaces, and operational procedures. Mentor junior engineers and contribute to engineering standards and technical hiring. Required Qualifications Approximately 5 or more years of hands-on experience in AI, machine learning, deep learning, computer vision, video intelligence, or related systems. Strong programming skills in Python . Strong practical experience with PyTorch , TensorFlow, or a comparable deep-learning framework. Demonstrated experience taking AI or ML systems from experimentation through production deployment. Strong understanding of neural-network architectures, model training, evaluation, inference, and optimisation. Experience working with at least one of the following areas: computer vision or video understanding; multimodal AI; generative AI; speech or audio processing; machine-learning systems; large language models; temporal or sequential modelling. Experience building APIs, inference services, or processing pipelines using technologies such as FastAPI, Docker, Kubernetes, cloud services, or distributed workers. Ability to understand technical papers and convert r…