Teams at Integrant

Build and operate production-grade enterprise AI agents across their full lifecycle, including architecture, orchestration, retrieval, evaluation, testing, observability, security, governance, and enterprise integrations. The role requires shipping multi-step LLM workflows, implementing RAG and MCP solutions, integrating APIs and workflow engines, establishing quality and reliability metrics, and mentoring engineers. Candidates need strong Python or TypeScript expertise, production LLM experience, and familiarity with agent orchestration, security, evaluation, and cloud deployment.

Designs and optimizes CI/CD pipelines across on-premise and cloud environments; deploys and manages containerized systems using Docker and Kubernetes; administers cloud platforms, Linux systems, monitoring tools, and infrastructure-as-code with Terraform and Ansible. Automates processes with Bash and Python, applies cybersecurity best practices, and collaborates with development teams throughout the software development lifecycle.

Leads manual and automated software testing across UI, system, integration, and API layers. Responsibilities include designing test plans and cases, prioritizing execution, managing defects, performing root-cause analysis, reporting results, and improving testing processes. The role also develops unit tests, test-support features, API documentation, and minor fixes; tests AI and agentic applications; supports Agile Scrum teams; and uses CI/CD, automation, database, and performance-testing tools.