Senior Software Engineer

• Design and build scalable end-to-end enterprise systems, bridging advanced frontends, mobile applications, and high-performance async backends.
• Deploy, manage, and optimize the production inferencing of large machine learning models (TensorFlow, PyTorch) and Large Language Models (LLMs) within Agentic AI architectures.
• Define robust GraphQL integration contracts to seamlessly connect AI outputs with core enterprise services.
• Translate ambiguous business needs into concrete technical architectures and actionable engineering tasks.
• Lead cross-functional engineering teams through the full delivery lifecycle to drive measurable business ROI.
• Manage stakeholder expectations regarding realistic AI capabilities, mitigating risks, and defining strict project boundaries.
• Optimize system performance across compute, memory, and network layers, including low-latency LLM inference and cost-efficient resource utilization (GPU/CPU).
• Design secure systems with robust authentication/authorization (OAuth, RBAC), data privacy safeguards, and defenses against AI-specific threats such as prompt injection and data exfiltration.
• Design and build scalable, event-driven systems that can handle high traffic reliably, using best practices to ensure data consistency and safe processing (e.g., avoiding duplicate or lost events).
• Design and build scalable end-to-end enterprise systems, bridging advanced frontends, mobile applications, and high-performance async backends.
• Deploy, manage, and optimize the production inferencing of large machine learning models (TensorFlow, PyTorch) and Large Language Models (LLMs) within Agentic AI architectures.
• Define robust GraphQL integration contracts to seamlessly connect AI outputs with core enterprise services.
• Translate ambiguous business needs into concrete technical architectures and actionable engineering tasks.
• Lead cross-functional engineering teams through the full delivery lifecycle to drive measurable business ROI.
• Manage stakeholder expectations regarding realistic AI capabilities, mitigating risks, and defining strict project boundaries.
• Optimize system performance across compute, memory, and network layers, including low-latency LLM inference and cost-efficient resource utilization (GPU/CPU).
• Design secure systems with robust authentication/authorization (OAuth, RBAC), data privacy safeguards, and defenses against AI-specific threats such as prompt injection and data exfiltration.

• Design and build scalable, event-driven systems that can handle high traffic reliably, using best practices to ensure data consistency and safe processing (e.g., avoiding duplicate or lost events).