Role Overview:

As a System Architect specializing in Agentic AI Infrastructure, you will be the technical visionary responsible for designing and scaling the backbone of our next-generation autonomous AI systems. You will lead the architectural evolution of our agentic frameworks, ensuring that complex LLM-driven workflows are performant, reliable, and secure. In this role, you will collaborate closely with cross-functional engineering teams, product managers, and data scientists to translate ambitious AI product roadmaps into robust distributed systems. Your work will directly influence the efficiency of our AI agents, enabling them to execute multi-step reasoning tasks at scale and delivering transformative value to our global customer base.

Key Responsibilities:

- Design and implement highly scalable distributed systems that support complex Agentic AI workflows, ensuring low-latency execution and high availability for mission-critical applications.

- Architect end-to-end MLOps pipelines that integrate LLMs and agentic frameworks into production environments, streamlining the deployment and monitoring of intelligent models.

- Lead the integration of advanced orchestration tools and LangChain-based workflows to enable seamless communication between AI agents and external enterprise systems.

- Optimize cloud infrastructure and Kubernetes-based deployments to balance computational costs with the high-performance requirements of real-time Generative AI processing.

- Establish best practices for prompt engineering and model fine-tuning at the architectural level to improve the accuracy and reliability of autonomous agent outputs.

- Mentor senior engineering talent and foster a culture of technical excellence, ensuring that architectural decisions align with long-term business scalability and security standards.

Required Skillset:

- Demonstrated expertise in designing complex System Architectures and Microservices, with a proven track record of managing distributed systems in high-traffic cloud environments.

- Advanced proficiency in Python and modern AI frameworks, specifically leveraging LangChain and LLM-based architectures to build autonomous agentic solutions.

- Strong command over Kubernetes and container orchestration, with the ability to manage the lifecycle of AI-heavy workloads across hybrid or multi-cloud infrastructures.

- Proven ability to communicate complex technical strategies to non-technical stakeholders, ensuring alignment between engineering output and business objectives.

- Exceptional problem-solving skills with a focus on MLOps and API Integration, capable of navigating the challenges of deploying experimental AI models into stable production environments.

- A collaborative mindset suited for a distributed work environment, demonstrating the self-discipline and communication skills required to thrive in a remote-first setting.

- A degree in Computer Science, Engineering, or a related quantitative field, complemented by 7 - 10 years of professional experience in building scalable software and AI infrastructure.