Senior Software Engineer

Job Location- Remote, India

Full Time

The company is building AI engineering and infrastructure services focused on AI security, token efficiency and deployment readiness. We are hiring an early engineering team to build a reusable secure AI factory foundation and extend it through customer projects.

Your role
Own detailed software design and hands-on implementation under an architect’s direction. Build reusable automation and integrations, review code and guide two junior or mid-level engineers. The secure AI factory scope is the software layer connecting models, deployment workflows, security controls and basic observability.

What you will do
Build Python services and APIs for model access, provider configuration and integration with approved cloud and frontier LLM endpoints.
Implement policy-based LLM routing, timeouts, retries and fallbacks, respecting model eligibility, data-handling constraints, quality, latency and cost.
Automate installation, configuration, upgrades and smoke testing of selected agentic platforms using Docker and scripted deployment workflows.
Implement secrets handling, access controls and audit events. Apply safe logging practices that avoid exposing credentials or sensitive prompts and responses.
Instrument request latency, errors, token usage and estimated model cost. Build basic dashboards and alerts; document pricing assumptions and reconcile estimates where usage data permits.
Create reusable connectors, deployment templates, automated tests and runbooks. Review code, troubleshoot integrations and separate shared components from customer configuration.

What you bring
Strong Python backend development, REST APIs, authentication, asynchronous workflows and production troubleshooting.
Hands-on Docker, Linux, Git, automated testing and CI/CD; practical integration experience with at least one cloud environment.
Ability to translate architecture guidance into maintainable components, explain tradeoffs and mentor other developers.
Candidates must be familiar with AI-assisted development (“vibe coding”) tools to accelerate development, troubleshooting and bug fixes, while reviewing and testing generated code for quality and security.
Clear written communication, ownership and effective collaboration in a remote team.
Useful additional experience
LLM APIs or gateways, agent frameworks, observability instrumentation, infrastructure automation and production AI integrations. Experience with one relevant AI deployment is particularly valuable; expertise in every platform is not required.

Scope and collaboration
Work with the architect and infrastructure and security specialists. Infrastructure teams own underlying GPU and cluster provisioning; this role owns software integration and automation. Success balances security, token cost, quality and deployment time.