Responsibilities
- Lead and oversee full-cycle engineering teams to deliver end-to-end software solutions with high quality and ownership.
- Architect, develop, and maintain scalable backend systems, distributed services, and robust APIs.
- Drive technical execution across teams, ensuring adherence to best practices and high code quality standards.
- Develop and refine CI/CD pipelines, automated testing frameworks, and secure deployment workflows to enhance delivery speed and reliability.
- Optimize cloud infrastructure on AWS for scalability, resilience, cost efficiency, and operational performance.
- Support live systems to meet service level objectives, improving availability, performance, and incident resolution processes.
- Collaborate with product, engineering, and business teams to convert business needs into scalable technical solutions.
- Diagnose and resolve complex production issues, conduct root cause analysis, and implement sustainable fixes.
- Mentor software engineers and foster a culture of accountability, teamwork, and continuous technical growth.
Work Arrangement
Remote — Hyderabad
Our Culture: The “Real” Deal
- Growth through practice: learning is embedded in daily work, with structured upskilling paths and role-specific certifications.
- Sustainable performance: 3 hours of protected learning time weekly to support development without burnout.
- Create lasting impact: collaborate with technical leaders to evolve platform architecture and reduce system dependencies.
AI-first engineering at energytec.ai
- Engineers lead in a human-led, agent-operated environment, using AI responsibly to accelerate delivery and improve system quality and resilience.
- Integrate AI throughout the software development lifecycle, including design, coding, testing, security, documentation, and incident management.
- Enforce mandatory safeguards: code reviews, data privacy, security controls, and governance policies are strictly upheld.
- Measure success through service level objectives, DORA metrics, and performance-cost efficiency.
Other
- 3 hours of protected learning time per week included in work hours.
- AI is applied across all stages of the SDLC: design, coding, testing, security, documentation, incident analysis, and runbook automation.
- Code reviews, security protocols, and governance are required and non-negotiable.
- Performance and impact are tracked using SLOs, DORA metrics, and cost-efficiency benchmarks.
- Encourage knowledge sharing by standardizing AI prompts, operational playbooks, and reusable automation tools.
