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What skills does Safe Security want for this Software Development Engineer III role?

The job description asks for Technical Leadership, Architecture and Design, Code Review, Cloud Expertise, Problem Solving, Collaboration, Project Execution and Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Make sure your resume shows the ones you have, in the words the job uses: applicant tracking systems look for them.

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Is this Software Development Engineer III job remote or onsite?

This is based in Bengaluru, Karnataka.

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Software Development Engineer III

Safe Security

Bengaluru, Karnataka, India

Job Overview:

We are seeking an experienced and passionate Engineer to join our engineering team. As a senior engineer, you will lead a high-performing engineering team working closely with our Design, Product, and other stakeholders to develop and deliver core product features that help in predicting and preventing cyber breaches.

Location: Bengaluru

Core Responsibilities:

Technical Leadership: Provide technical leadership and mentorship to engineers, promoting engineering best practices, high code quality, and continuous improvement.
Architecture and Design: Collaborate with cross-functional teams to design and architect scalable, secure, and high-performance multi-tenant services and systems.
Code Review: Conduct thorough code reviews, identify opportunities for improvement, and provide constructive technical feedback to engineers.
Cloud Expertise: Leverage AWS services such as Lambda, API Gateway, EC2, S3, RDS, and other appropriate cloud technologies to architect and optimize cloud-based solutions.
Problem Solving: Troubleshoot complex technical problems, identify root causes, and design effective and scalable solutions.
Collaboration: Work closely with Product, Design, Engineering, and other stakeholders to ensure technical solutions are aligned with product and business objectives.
Project Execution: Lead the planning, execution, and successful delivery of features while maintaining engineering quality and predictable delivery.

Experience/ Qualifications/ Education:

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
5+ years of software engineering experience, including experience operating as a Senior Engineer or Lead Engineer.
Strong expertise in at least one server-side programming language such as Node.js, Python, or Go.
Hands-on experience designing and developing APIs and backend services. Experience with TypeScript and frameworks such as Express is preferred.
Good understanding of SQL and NoSQL databases and their appropriate use across different workloads.
Strong understanding of cloud computing concepts and hands-on experience with a major cloud provider such as AWS, GCP, or Azure.
Experience building and deploying applications in containerized environments.
Proven track record of building and delivering high-performance and scalable platforms in a fast-paced engineering environment.
Strong understanding of system design, distributed systems, scalability, reliability, and multi-tenant architectures.
Strong leadership and communication skills, with the ability to lead and mentor engineers toward technical excellence.
Strong problem-solving and critical-thinking abilities.
High degree of ownership with the ability to drive problems from definition through production delivery.
Understanding of DevOps practices and Infrastructure as Code technologies such as AWS CloudFormation is advantageous.

AI / LLM Skills:

Strong working knowledge of Generative AI and LLM fundamentals, including how LLMs work, tokens, context windows, prompting, hallucinations, embeddings, and common model limitations.
Hands-on experience using AI-assisted development tools such as Cursor, Claude Code, GitHub Copilot, or equivalent, with the ability to critically review and validate AI-generated code, tests, and technical solutions.
Understanding of common LLM application patterns, including structured outputs, tool/function calling, retrieval-augmented generation (RAG), and agentic workflows.
Ability to make informed engineering decisions about when to use deterministic software, a direct LLM call, retrieval/RAG, or an agentic approach, based on the problem being solved.
Ability to define how AI-powered capabilities should be evaluated, including the use of golden datasets, appropriate accuracy metrics, regression testing, human evaluation, and LLM-as-a-judge where appropriate.
Ability to reason about production AI trade-offs, including accuracy, latency, cost, reliability, security, and observability.

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