Software Engineer II - Platform & Infrastructure

United States Digital Space LLC; Bengaluru; Hybrid; INR 2,400,000 - 4,200,000; Full time

Job summary

Abnormal AI is seeking a Platform & Infra software engineer in Bengaluru to own observability platforms and build scalable infrastructure. You will collaborate with engineering teams to design and deliver critical platform features, maintain metric pipelines, dashboards, and alerting, and drive reliability across production environments.

You will design and operate end‑to‑end services, mentor peers, and push for automated resilience and cost‑efficient observability across US/EU/GovCloud as

Qualifications

• 4+ years of hands‑on backend engineering experience designing, building, and operating production‑grade distributed systems.
• Strong proficiency in Python — the primary language for Airflow DAGs, platform services, and automation tooling.
• Working proficiency in Golang — used for high‑performance infrastructure components, metric pipelines, and platform services.
• Experience building systems that process data at scale across metric ingestion pipelines, stream/batch processing, or high‑throughput APIs.
• Demonstrated experience owning a service end‑to‑end — from design through production deployment, monitoring, and iteration.

Responsibilities

• Design, develop, and deliver key platform features from design docs to production rollout.
• Own features end‑to‑end: scoping, implementation, testing, deployment, and post‑launch monitoring.
• Take ownership of 1–3 key services within Observability or Data Infra and ensure reliability and evolution.
• Participate in on‑call rotations to triage and resolve production issues independently.
• Improve system resilience by automating runbooks and refining SLAs/SLOs.
• Build platforms and APIs that simplify deployments and observability for other teams.

Skills

Backend engineering

Distributed systems

Python

Golang

Observability

Tools

Prometheus

Chronosphere

Grafana

PagerDuty

Airflow

Spark

Job description

About the Role

Abnormal AI is looking for an experienced and driven Platform & Infra software engineer to join the PI team. Join us and help build the platforms that power Abnormal's growth

• Observability Platform - Own and evolve the monitoring, metrics, and alerting infrastructure that every engineering team at Abnormal depends on. You'll work across the Prometheus, Chronosphere, and Grafana stack to ensure engineers can see what their systems are doing in real time — building dashboards, managing metric pipelines at scale, operating the PagerDuty alerting pipeline, and driving cost-efficient observability across all production environments (US, EU, and GovCloud).

Your Impact

• Own the observability stack (Prometheus, Chronosphere, Grafana, PagerDuty) that every team relies on to detect, diagnose, and resolve production issues — when you make it better, every engineer at Abnormal gets faster.

• Design platforms and developer tooling that remove friction — reducing deployment times, simplifying pipeline authoring, and letting product teams focus on building rather than firefighting.

• Drive SLAs and SLOs for critical shared infrastructure ensuring the systems behind our products are resilient and cost-efficient.

• Your architectural decisions on alerting pipelines and cross‑environment deployments will define what products we can build and how quickly we deliver them to customers.

What you will do

• Work with the Tech Lead, Engineering Manager, and Product Manager to design, develop, and deliver key platform features — from technical design docs through production rollout

• Own features end‑to‑end: scoping, implementation, testing, deployment, and post‑launch monitoring across multiple environments (US, EU, GovCloud)

• Take ownership of 1-3 key services within Observability (Prometheus, Chronosphere, Grafana, PagerDuty pipeline) or Data Infra (Airflow, Spark) and be accountable for their reliability, performance, and evolution

• Participate in on‑call rotations — triage, diagnose, and resolve production issues independently, building deep operational knowledge of the systems you own

• Improve system resilience by converting runbooks into automated solutions, refining SLAs/SLOs, and proactively identifying performance bottlenecks and failure modes

• Assume ownership of the reliability of everything you build, including comprehensive unit tests, integration testing, and observability instrumentation

• Build platforms, tooling, and APIs that make it easier for other engineering teams to ship — whether that's faster pipeline deployments, better dashboards, or simpler alerting configuration

• Partner with internal customers (product and engineering teams) to understand their needs and translate them into scalable platform capabilities

• Communicate effectively in an async‑first, distributed environment — proactively providing updates, discussing challenges, and proposing solutions without prompting

• Mentor junior engineers on the team, helping them ramp up on service operations and development practices

• Raise the bar of engineering excellence through code reviews, knowledge sharing, design discussions, and contributing to team best practices

Must Haves

• Backend Engineering & Distributed Systems (4+ years)

• 4+ years of hands‑on backend engineering experience designing, building, and operating production‑grade distributed systems

• Strong proficiency in Python — the primary language for Airflow DAGs, platform services, and automation tooling

• Working proficiency in Golang — used for high‑performance infrastructure components, metric pipelines, and platform services

• Experience building systems that process data at scale — whether metric ingestion pipelines, stream/batch processing, or high‑throughput API services

• Demonstrated experience owning a service or platform end‑to‑end — from technical design through production deployment, monitoring, and iteration

• Comfortable balancing feature development with operational responsibilities: you've shipped features and kept them running reliably at scale

• Experience writing technical design documents that articulate trade‑offs, propose solutions, and get buy‑in from peers and tech leads

• Track record of breaking down ambiguous problems into concrete, deliverable milestones

• Experience with fault tolerance patterns — retries, circuit breakers, graceful degradation, backpressure — and knowing when to apply each

• Proven incident response capability: you've been on‑call, diagnosed production issues under pressure, and driven them to resolution

• Strong testing discipline — unit tests, integration tests, and an understanding of what to test and how to keep test suites maintainable

• Ability to design systems with a forward looking perspective — thinking about how your architecture handles 10x growth, multi‑region deployment, and evolving requirements

• Ability to contribute to and influence cross‑team technical direction — you're not just implementing specs, you're shaping the solution

• Async‑first communication excellence — strong written communication skills for design docs, Slack discussions, PR reviews, and status updates across time zones

• Proactive communicator — you surface blockers early, share con

• Solid understanding of monitoring, alerting, and observability principles — you've instrumented services, set up dashboards, defined SLIs/SLOs, or triaged production incidents using metrics and logs

Nice to Have

• Hands‑on experience with Prometheus — PromQL queries, recording rules, alerting rules, relabeling configs, and understanding metric cardinality challenges at scale

• Experience with Grafana — building dashboards, templating, managing datasources, and creating meaningful visualizations for operational and business metrics

• Familiarity with commercial observability platforms like Chronosphere, Datadog, New Relic, or Honeycomb — understanding trade‑offs between self‑hosted and managed solutions

• Experience designing or operating an alerting pipeline — PagerDuty, OpsGenie, or similar — including alert routing, escalation policies, and reducing noise/alert fatigue

Cloud Infrastructure & Kubernetes

• Familiarity with AWS services — EC2, ECS, EKS, S3, RDS, IAM, CloudWatch, Lambda, SQS/SNS — and understanding how to architect cost‑effective, secure cloud infrastructure

• Experience with Kubernetes (K8s) — deploying and operating workloads, understanding pods/services/deployments, Helm charts, and debugging cluster‑level issues

• Exposure to Infrastructure-as-Code tools — Terraform, Pulumi, or CloudFormation — and understanding the value of declarative infrastructure management

• Experience with CI/CD pipelines — GitHub Actions, Jenkins, or similar — and optimizing build/deploy times for platform services

Programming & Framework

• Experience with Django or similar Python web frameworks — building APIs, managing migrations, and understanding ORM performance characteristics

• Familiarity with gRPC or protobuf for inter‑service communication in a microservices architecture

Technical Leadership & Platform Thinking