SDE III - Engineering Productivity (AI)
Safe Security3 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Instrument engineering workflows and quantify bottlenecks including cycle time, review latency, CI failures, test authoring, operational toil, and onboarding.
- Select and implement AI, automation, conventional software, or process changes based on measurable bottlenecks.
- Apply AI to development, code review, testing, documentation, releases, design analysis, migrations, debugging, incident investigation, and root-cause analysis.
- Build production engineering agents for CI diagnosis, production investigations, test writing, dependency upgrades, security fixes, pull-request analysis, and migrations.
- Redesign code review, testing, CI, and incident processes with appropriate human ownership and approval gates.
- Build AI gateways, agent runtimes, workflow orchestration, codebase intelligence, context retrieval, and integrations with Git, CI, observability, and ticketing systems.
- Provide permission-aware access to code, architecture documentation, ownership, deployment state, telemetry, incidents, and standards.
- Implement quality, security, privacy, access-control, auditability, and evaluation guardrails for AI-generated changes.
- Baseline results, run experiments, measure impact, drive adoption, and mentor engineering teams.
- Deliver initial quantified bottleneck analysis and a production automation within the first 90 days.
Requirements
- 6+ years building production software with SDE3-level ownership of production systems.
- Strong Python, Java, Go, or equivalent programming experience, including shipping production services and reviewing code.
- Strong grounding in distributed systems, APIs and microservices, and software architecture.
- Hands-on cloud infrastructure and CI/CD experience.
- Demonstrated developer tooling and automation work adopted by other engineers.
- Practical experience with LLMs and agentic systems in real production systems rather than only experiments.
- Ability to reason quantitatively using baselines, hypotheses, experiments, and measured results.
- Strong writing and ability to influence engineering teams without formal authority.
- Preferred experience with LLM APIs, model orchestration, production AI agents with tool use, RAG, retrieval, vector databases, MCP or similar protocols, LLM evaluation, prompt and context engineering, AI observability, GitHub or GitLab APIs, Kubernetes, cloud platforms, internal developer platforms, DORA or SPACE-style measurement, and security- or compliance-bound environments.
- Expected to identify organization-wide productivity problems, build scalable solutions, prove improvements quantitatively, and mentor engineers.
Benefits
- Meaningful equity for employees.
- Unlimited leave.
- Comprehensive medical insurance and wellness benefits.
- Career advancement opportunities in a rapidly growing company.
Tech Stack
Categories
About Safe Security
Safe Security builds an AI-driven cyber risk quantification and management platform used by CISOs, GRC, and third-party risk teams to measure and prioritize enterprise, vendor, and AI-related risks. It sells its software to large enterprises as a subscription service, integrating with security and business systems to produce board-level, dollar-based risk insights. Founded in 2012 and headquartered in Palo Alto, it is privately held.