Snr Applied AI Architect
Posted Aug 7, 2026
About the role
A client of byteSpark.ai is seeking a Senior Applied AI Architect to lead the architecture and delivery of advanced AI solutions for high-volume telecommunications and government use cases. The successful candidate will define end-to-end architectures spanning large language models, natural language processing, speech recognition, computer vision, diffusion models, and multimodal analytics. This role will translate complex business, security, data, and performance requirements into documented system components, interfaces, deployment patterns, and implementation roadmaps. The architect will guide engineering teams through model selection, fine-tuning, evaluation, integration, optimization, and reliable production deployment. They will design scalable machine learning pipelines capable of processing real-time and batch voice, text, image, and data streams containing billions of data points. The position requires ownership of key technical decisions involving distributed training, inference infrastructure, MLOps, privacy, observability, resiliency, and cost-performance tradeoffs. The architect will collaborate with product, data, platform, security, and client stakeholders to integrate AI capabilities with existing applications, data sources, cloud platforms, and enterprise systems. They will establish standards for responsible AI, reproducibility, model monitoring, technical documentation, and measurable solution quality. The role also includes mentoring engineers, communicating architecture to technical and nontechnical audiences, and evaluating emerging AI methods that can deliver practical business and operational value.
Requirements
- At least 7 years of relevant AI or machine learning experience, including senior ownership of production AI architecture and delivery across complex initiatives.
- Expert-level Python skills and substantial hands-on experience with PyTorch, TensorFlow, JAX, Hugging Face Transformers, or comparable deep learning frameworks.
- Demonstrated expertise architecting large language model and NLP solutions using transformer architectures such as GPT, BERT, T5, LLaMA, or comparable models.
- Hands-on experience with speech and audio processing architectures, including ASR technologies such as Whisper or Wav2Vec2 and real-time or batch voice pipelines.
- Proven ability to design and deploy end-to-end multimodal AI pipelines encompassing data ingestion, model training, evaluation, serving, integration, monitoring, and continuous improvement.
- Strong knowledge of model adaptation and optimization techniques, including LoRA, QLoRA, instruction tuning, quantization, pruning, distillation, ONNX, or TensorRT.
- Production experience with distributed computing and MLOps technologies such as Docker, Kubernetes, MLflow, Ray, Apache Spark, DVC, and cloud-based machine learning platforms.
- Ability to translate business, security, scalability, and performance requirements into documented AI architectures, interfaces, implementation plans, and defensible technical decisions.
Desirable skills
- Advanced degree in computer science, machine learning, data science, mathematics, or a closely related discipline.
- Experience with diffusion models, synthetic data generation, multimodal content generation, VAEs, GANs, or related generative modeling methods.
- Knowledge of responsible and privacy-preserving AI, including explainability, bias detection, federated learning, adversarial robustness, or secure government deployments.
- Experience with real-time streaming inference, edge deployment, hardware-aware optimization, or high-volume telecommunications data processing.
- Published AI research, open-source contributions, patents, conference presentations, or demonstrated leadership in advancing engineering practices.