Architect & Build: Design and deliver agentic workflows and scalable microservices, primarily using Python, within a distributed system architecture.
AI Integration: Lead the integration of Generative AI capabilities, including LLM APIs, RAG pipelines, and agentic workflows into our production systems.
Agentic Operations & Observability: Implement operational practices, observability, and autonomous monitors (e.g., detecting intelligence drift or hallucinations) to ensure AI reliability in production.
Elevate Engineering: Drive overall engineering quality and speed by implementing AI-SDLC practices, ensuring high standards through rigorous code reviews, test automation, and by establishing continuous, trace-based quality evaluation for all software and agentic components.
Mentor & Innovate: Evaluate emerging AI tooling, mentor fellow engineers, and contribute directly to our technical roadmap and Agentic AI-SDLC and Production Operations.

7+ years of backend or full-stack engineering experience, including 2+ years building production-grade agentic systems in distributed environments.
Strong proficiency in Python (preferred) or TypeScript/Go/Rust, with the ability to transition to Python.
Hands-on experience with agentic frameworks such as LangChain, LangGraph, CrewAI, DeepAgents, OpenClaw, Hermes, or equivalent.
Proven expertise in designing cloud-native, event-driven architectures using AWS, Kubernetes, Terraform, CI/CD, GraphQL or REST APIs, and messaging systems (SQS/SNS).
Experience with PostgreSQL, DynamoDB, MongoDB, and preferably vector databases.
Familiarity with AI-native development tools such as Claude Code, Cursor, or Copilot.
Excellent communication skills and the ability to work independently in a highly autonomous environment.
Experience with React, Next.js, or similar frontend technologies.

Software Engineer – Full-Stack, Backend, Applied AI

Key skills