SDE III - Engineering Productivity (AI)
Safe Security (company site, opens in a new tab)6+ yrsBengaluruOn-siteDirect career pageEst. 30–50 LPA
Posted on 18 Sept
Reach people at Safe Security
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Safe Security you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Identify and quantify engineering productivity bottlenecks across the full development lifecycle and prioritize solutions to recover engineer time.
- Design and build production-quality AI-driven agents, automation, platform capabilities, and process changes to improve engineering throughput, adoption, and measurable cycle time improvement.
- Redesign engineering workflows incorporating AI for tasks like code review, test generation, incident diagnosis with guardrails to maintain quality and security, and prove impact through quantitative metrics.
Minimum Requirements
- 6+ years of professional software development experience at SDE3 level with ownership of production systems.
- Proficiency in Python, Java, Go, or equivalent with experience shipping production services and reviewing code.
- Strong understanding of distributed systems, APIs, microservices, software architecture, cloud infrastructure, and CI/CD workflows.
- Practical experience implementing LLMs and agentic AI systems in real products, and ability to quantitatively measure and influence engineering workflows.
Ideal Candidate Profile
- Experienced in tackling org-wide engineering productivity challenges autonomously without needing a given backlog, with ability to influence cross-functional teams without authority.
- Capable of building scalable, robust AI and automation systems integrated deeply into engineering lifecycles (e.g., CI, code review, incident response).
- Comfortable driving adoption through data-driven experiments and mentoring teams to embrace AI-first engineering workflows aligned to measurable impact.