About the role
Responsibilities:
- Tekion is looking for talented Staff Software Engineers who want to be part of building next-gen AI-native business applications in the cloud.
- You will use your passion and expertise for creating world-class AI platform products.
- You should excel in working with large-scale applications and frameworks and have outstanding communication and leadership skills.
- Lead the technical vision and architecture for Tekion's AI Native Platform, the foundational layer powering AI capabilities across all Tekion products.
- Architect and build Tekion's AI gateway, a highly available, multi-provider LLM abstraction layer.
- Lead the design of Tekion's MCP (Model Context Protocol) ecosystem.
- Design and implement agentic AI frameworks.
- Drive AI platform reliability and observability.
- Provide technical mentorship and set engineering standards for the AI Native Platform team.
- Take end-to-end ownership of platform architecture decisions and be responsible for their success in production.
- Collaborate with product, AI/ML research, and domain engineering teams to define the AI platform roadmap.
- Deliver with quality, on time, in a fast-paced start-up environment.
Requirements:
- Bachelor/master's in computer science or a relevant field.
- 9+ years of relevant software engineering experience.
- Strong sense of ownership and technical leadership.
- Excellent Java and object-oriented development skills.
- Deep experience in building and scaling microservices.
- Strong problem-solving skills, technical troubleshooting, and diagnosis.
- Expected to be a role model for engineers, enforce high code quality, and drive engineering excellence within the team.
- Experience in developing backends for enterprise systems (eCommerce / manufacturing/supply chain, etc. )
- Excellent understanding of debugging, performance, and optimisation techniques.
- Experience with Java, MongoDB, MySQL, AWS technologies, ELK stack, Spring Boot, Kafka.
- Deep experience in cloud technologies and large-scale distributed systems.
- Deep expertise in LLM systems model selection and evaluation, token economics at scale, context window optimisation.
- Strong knowledge of MCP (Model Context Protocol) and tool-use standards, designing MCP ecosystems, tool schema governance, auth and trust models for MCP servers, and integration with multi-agent frameworks.
- Experience designing AI gateways / LLM infrastructure.
- Experience with agentic AI architectures, multi-agent systems, planning/reasoning frameworks (ReAct, Plan-and-Execute), tool orchestration, guardrails and safety layers, human-in-the-loop escalation, and agent evaluation.
- Proficiency in LLM observability and MLOps.
- Experience with AI safety, governance, and responsible AI patterns.
- Demonstrated ability to deliver in a fast-paced environment.
- Excellent communication and leadership skills.