Senior Software Engineer - Message Security Detection

Menlo Ventures; Bengaluru; On-site; INR 3,500,000 - 7,500,000; Full time

Job summary

Abnormal AI in Bengaluru is seeking a Tech Lead (SWE-3) to own the architectural blueprint for distributed data pipelines and cloud-native infrastructure. You will mentor engineers and translate complex requirements into scalable solutions while driving best practices.

You will lead AI productivity initiatives, leveraging Claude, OpenAI, and Copilot to accelerate development and ensure secure AWS/EKS operations. 8+ years of backend experience and strong system design are required.

Qualifications

• 8+ years of professional backend experience in production systems.
• Expert-level proficiency in building and optimizing distributed data pipelines using Spark and Airflow.
• Cloud experience on AWS (highly preferred), GCP or Azure with hands-on container orchestration on EKS.
• Leadership and mentorship of engineers and contribution to long-term technical strategy.
• Strong system design skills to handle high throughput and latency challenges.
• Experience with PostgreSQL or similar relational databases at scale.
• BS in Computer Science, Applied Sciences, or related field.

Responsibilities

• Architect and scale high-throughput detection systems and backend services for low latency and high reliability.
• Lead data orchestration by developing robust data pipelines with Spark and Airflow.
• Provide technical leadership, define roadmaps, mentor engineers, and promote code quality and design best practices.
• Drive AI productivity initiatives using tools like Claude, OpenAI, and GitHub Copilot to accelerate development lifecycles.
• Collaborate with TPMs, Product Managers, Data Scientists, and Security researchers to translate requirements into scalable tech solutions.
• Manage and optimize cloud-native infrastructure on AWS/EKS to ensure cost-effectiveness, security, and performance.

Skills

Python

Golang

Distributed data pipelines

Spark

Airflow

AWS

GCP

Azure

Leadership

System design

PostgreSQL

Education

BS in Computer Science

Tools

Spark

Airflow

Kubernetes

ETL tooling

Job description

The Mission

We are building the high-throughput, low-latency infrastructure that powers our real-time threat detection capabilities. This isn't just about maintaining stable backend systems; it’s about architecting a cutting-edge data platform at massive scale to turn raw data into high-fidelity, actionable intelligence for our ML models and security researchers.

As a Tech Lead (SWE-3) on this team, you will be our technical anchor and force multiplier. You will own the architectural blueprint for our distributed data pipelines (Spark/Airflow) and cloud-native infrastructure (AWS/EKS), bridging the gap between complex system design and strategic execution. Beyond traditional engineering, you will lead by example spearheading our AI productivity initiatives to supercharge how our entire engineering team builds, tests, and ships software. If you are passionate about deep technical ownership, mentoring the next generation of engineers, and defining the future of our data ecosystem, this is your mission.

What You Will Do

• Architect & Scale: Design and maintain high-throughput detection systems and backend services, ensuring low latency and high reliability.
• Data Orchestration: Lead the development of robust data pipelines usingSpark and Airflow to supply detection teams with high-fidelity data for ML model training and evaluation.
• Technical Leadership: Partner with Engineering Managers to define technical roadmaps, mentor junior/mid-level engineers, and drive best practices in code quality and system design.
• AI-Driven Innovation: Lead the team in adopting AI productivity initiatives, leveraging tools like Claude, OpenAI, and GitHub Copilot to accelerate the development lifecycle and automate internal workflows.
• Cross-functional Collaboration: Work closely with TPMs, Product Managers, Data Scientists, and Security Researchers to translate complex business needs into scalable technical solutions.
• Operational Excellence: Manage and optimize cloud-native infrastructure on AWS/EKS, ensuring systems are cost-effective, secure, and performant.

Must Haves

• 8+ years of professional experience: Proven track record in production-level backend development with Python or Golang.
• Data Engineering Expertise: Expert-level proficiency in building and optimizing distributed data pipelines using Spark and Airflow (or equivalent orchestration tools).
• Cloud & Infrastructure: Extensive experience managing and scaling cloud-native applications on AWS (highly preferred), GCP, or Azure, with a proven track record of hands‑on container orchestration using EKS.
• Leadership & Mentorship: Experience leading technical projects, mentoring engineers, and contributing to the long‑term technical strategy of a team.
• System Design: Strong ability to design complex integrations and handle significant throughput/latency challenges.
• Problem Solving: A methodical approach to performance debugging, benchmarking, and resolving bottlenecks in large‑scale systems.
• Database Proficiency: Strong experience with Postgres or similar relational databases at scale.
• Education: BS in Computer Science, Applied Sciences, or a related engineering field.

Nice to Have

• Frontend Literacy: Familiarity with React and TypeScript to assist with internal tool visualizations.
• Data Ecosystems: Experience with Databricks, Snowflake, or similar data lakehouse architectures.
• Cybersecurity Domain: Background in threat detection, network security, or fraud prevention.
• Advanced Degrees: MS in Computer Science or Electrical Engineering.

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A note on AI in our process:
Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and areas for the interviewer to explore. They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person. Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards.

Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here. If you would like more information on your EEO rights under the law, please click here.