About the role
Responsibilities:
- Design and build production-grade AI platform services, LLM gateways, MCP servers, and agent orchestration frameworks.
- Lead the design and implementation of Tekion's AI gateway, a unified abstraction layer for multi-provider LLM access with intelligent routing, fallback, cost optimization, token budgeting, and comprehensive observability.
- Design and implement MCP (Model Context Protocol) infrastructure.
- Design agentic AI patterns, multi-step reasoning workflows, tool orchestration, guardrails, and human-in-the-loop escalation paths.
- Take end-to-end ownership of critical platform subsystems.
- Solve complex performance bottlenecks across inference and orchestration layers.
- Mentor junior and mid-level engineers.
- Communicate and collaborate with management, product, QA, UI/UX, and AI/ML teams.
- Deliver with quality, on time, in a fast-paced start-up environment.
Requirements:
- Bachelor's/Master's in computer science or relevant fields.
- 8+ years of relevant software engineering experience.
- Strong sense of ownership.
- Excellent Java and object-oriented development skills.
- Experience in building and scaling microservices.
- Strong problem-solving skills, technical troubleshooting, and diagnosis.
- Expected to be a role model for young engineers with a strong sense of code quality and to enforce code quality within the team.
- Strong knowledge of RDBMS and NoSQL technologies.
- Experience in developing backends for enterprise systems (e-commerce/manufacturing/supply chain, etc. ).
- Excellent understanding of debugging, performance, and optimization techniques.
- Experience with Java, MongoDB, MySQL, AWS technologies, the ELK stack, Spring Boot, and Kafka.
- Experience in developing large-scale cloud-native applications.
- Strong understanding of LLM fundamentals.
- Experience with or strong knowledge of MCP (Model Context Protocol), designing tool schemas, building MCP servers, tool discovery, and integrating MCP into agentic workflows.
- Understanding of AI gateway architecture: multi-provider LLM routing, semantic caching, load balancing, token tracking, cost allocation, fallback chains, and request/response logging.
- Familiarity with agentic AI frameworks like LangChain/LangGraph, LlamaIndex, CrewAI, OpenAI Agents SDK, or similar orchestration tools.
- Experience with LLM observability and evaluation tracing (LangSmith, OpenTelemetry), LLM-as-judge evaluation, and cost and latency monitoring.
- Understanding of AI safety and guardrails, input/output validation, PII detection, content filtering, and hallucination mitigation.
- Demonstrated ability to deliver in a fast-paced environment.
- Good communication skills.