• Architect and manage Kubernetes clusters tailored to AI/ML workloads. • Implement Run:ai and operators for GPU resource orchestration and workload scheduling. • Develop and maintain Python-based automation scripts and ML pipelines; automate infrastructure provisioning with Terraform and configuration management with Ansible. • Create and manage Jupyter Notebooks for experimentation and collaboration. • Integrate and optimize NVIDIA Enterprise Suite components (CUDA, NeMo Framework, Triton, TensorRT, GPU drivers) for accelerated computing. • Establish and maintain MLOps best practices for model lifecycle management, CI/CD, and monitoring (e.g., MLflow, Kubeflow). • Work closely with data scientists and platform engineers to ensure efficient resource utilization and scalability across environments.

• Develop and own the roadmap for Socure’s AI/ML platform, including data and feature engineering workflows, training infrastructure, experimentation tooling, model deployment/serving, monitoring, and governance. • Define architecture and standards that create clear, scalable, and secure paths for building and operating AI systems. • Assess technology options and drive consolidation across the company to reduce fragmentation and improve consistency across the ML toolchain. • Partner with Data Science, Engineering, Product, and Sales-Enablement teams to develop AI infrastructure that delights Customers. • Lead the design and operation of the end-to-end ML lifecycle: data ingestion, feature engineering, experimentation, training, model registry, deployment, and continuous monitoring. • Guide the team to deliver high-quality platform capabilities with predictable timelines and strong technical rigor. • Implement and enforce best practices around model versioning, auditability, lineage tracking, data governance, and security controls. • Lead, mentor, and grow both senior and junior ICs across ML infrastructure, MLOps, and distributed systems.

• Bridge the gap between research and real-world application. • Ensure high-performance infrastructure, automated pipelines, and deployment strategies. • Design and maintain scalable cloud environments (GCP/AWS) using Terraform. • Manage GPU/TPU resource allocation for training, fine-tuning, and interactive notebooks. • Build internal services and CLI tools for the AI team. • Design CI/CD and training pipelines using tools such as GitHub Actions, MLFlow, Vertex AI Pipelines. • Develop reusable patterns for model serving and manage service deployments to Kubernetes. • Manage and optimize vector databases and embedding pipelines for RAG-based systems. • Implement techniques to reduce latency and increase throughput.

• Design and build internal AI frameworks, SDKs, and shared libraries • Enable teams to integrate AI features with minimal friction • Set up standardized patterns for using LLMs, embeddings, agents, and workflows • Build reusable components for prompt management, evaluation, observability, and safety • Define best practices for AI usage, cost control, and reliability • Evangelise AI internally through documentation, examples, and hands-on guidance • Rapidly prototype AI-powered features and turn them into reusable building blocks • Own AI tooling from experimentation to production