Eiris

As a Senior Full-stack Engineer at Eiris Innovations, you will pioneer an agentic approach to software engineering. You will not only build cloud-native applications but also design the AI-driven systems that help develop, maintain, and monitor them. You will act as an architect and orchestrator, leveraging the latest in agentic coding to drastically increase development velocity while maintaining institutional-grade stability.

Key Responsibilities

• Agentic Software Orchestration: Lead the transition to an agentic coding workflow, utilizing AI agents to automate the end-to-end development lifecycle—from architectural planning and boilerplate generation to feature implementation and documentation.
• Cloud-Native Development: Design, develop, and maintain scalable front-end and back-end components of cloud-native applications using an AI-first approach to accelerate velocity without sacrificing quality.
• Agentic Maintenance & Evolution: Implement agent-based systems for the continuous updating and refactoring of legacy code, ensuring software evolves autonomously based on updated requirements or performance metrics.
• Autonomous Monitoring & Healing: Develop and deploy agentic monitoring solutions that not only detect anomalies in cloud-native environments but can propose and execute automated patches or scaling adjustments.
• Human-in-the-Loop (HITL) Governance: Establish rigorous review frameworks to validate AI-generated code, ensuring security, performance, and adherence to EIRIS standards.
• API & Data Orchestration: Create and maintain RESTful/GraphQL APIs and manage databases (PostgreSQL, MongoDB), leveraging AI to optimize query performance and schema evolution.
• Secure Infrastructure: Implement JWT/OAuth and manage CI/CD pipelines, integrating AI agents to automate security scanning and deployment validation.
• Technical Leadership: Mentor junior developers in “Prompt Engineering for Software Engineering,” teaching them how to steer agents effectively and audit AI outputs.

Qualifications

• Education: Bachelor’s degree or higher in Computer Science or a related field.
• Experience: 8-10 years of experience in Full Stack Development, with a proven track record of transitioning from traditional manual coding to AI-augmented workflows.
• Agentic Mastery: Demonstrated experience using agentic frameworks (e.g., AutoGPT, LangGraph, CrewAI, or advanced IDE agents like Cursor/Windsurf) to build and maintain complex software.
• Cloud-Native Expertise: Deep proficiency in AWS, Azure, or GCP, with a focus on serverless architectures and containerization (Docker, Kubernetes).
• Polyglot Proficiency: Ability to oversee and audit code across multiple stacks (React, Node.js, Python, Django) regardless of whether the code was human-written or agent-generated.
• Architectural Oversight: Strong understanding of microservices and distributed systems, with the ability to design “agent-friendly” architectures that are modular and easy for AI to navigate.
• Quality Assurance: Expert-level knowledge of automated testing frameworks; ability to design “test-driven” prompts that force agents to produce verifiable, bug-free code.
• Soft Skills: Exceptional analytical skills and the ability to communicate complex architectural visions to both human teams and AI agents.

Preferred skills

• Advanced AI Tooling: Experience with LLM orchestration layers (LangChain, LlamaIndex) and fine-tuning models for specific coding domains.
• Prompt Engineering: Mastery of advanced prompting techniques (Chain-of-Thought, Tree-of-Thoughts) specifically for software architecture and debugging.
• DevOps Automation: Experience building “AI-Ops” pipelines where agents handle deployment logs, error reporting, and automated rollbacks.
• Observability: Familiarity with modern observability tools (Prometheus, Grafana, Datadog) integrated with AI for predictive maintenance.
• Open Source Contribution: Experience contributing to or managing AI-driven open-source projects.

Success Metrics of the role

• Reduction in time-to-market for new features through agentic automation.
• Increase in test coverage via AI-generated edge-case suites.
• Reduction in Mean Time to Recovery (MTTR) through agentic monitoring.