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Principal Engineer (PMTS) – Backend & Platform

Ecolibrium

Bangalore Urban, Karnataka, India

Overview

We are looking for a Principal Member of Technical Staff (PMTS) to design, build and own the backend services and distributed systems that power our energy and IoT platform. This is a backend-first role: the PMTS will write production code every week, shape the architecture of high-throughput, event-driven services, and take those services all the way to production on AWS and Kubernetes.

This is a deeply hands-on senior individual contributor role. It requires very strong backend engineering, because that is where most of the work is; strong distributed systems depth, because our platform processes real-time telemetry at scale; and strong hands-on DevOps capability with AWS, Docker and Kubernetes, because engineers here own what they build through to production rather than handing it over. This is an AI-native engineering environment: AI coding agents such as Claude Code and Cursor are part of the daily workflow, and the PMTS will set the standard for how AI-assisted changes are reviewed. The role requires 8+ years of relevant experience.

THE WORK

Key Responsibilities

Backend Engineering (Hands-on)

Design, build and ship production-grade backend services in Java, Spring Boot, Python and Go, writing a significant share of the code yourself.
Design APIs, data models and service contracts with versioning, failure semantics and scaling paths considered up front.
Own performance of services end to end: profiling, query optimisation, caching, connection management and memory behaviour.
Set and enforce standards for code quality, automated testing (unit, integration and contract) and code review.
Modernise legacy services incrementally without disrupting production.
Choose the right technology for the problem rather than defaulting to a familiar stack.

Distributed Systems & Data

Design and build event-driven and streaming systems on Kafka, MQTT and Apache Flink for high-throughput IoT telemetry.
Make deliberate trade-offs on consistency, ordering, partitioning, idempotency and delivery guarantees.
Build for failure: retries with backoff, circuit breaking, backpressure, graceful degradation and safe replay.
Design data storage across PostgreSQL, Redis and time-series databases for scale, retention and query patterns.
Debug hard problems across service, database, messaging and network layers.

DevOps, AWS & Kubernetes

Containerise services with Docker and own their deployment on Kubernetes, including manifests, Helm, autoscaling, resource limits and health checks.
Design and operate AWS infrastructure for the services you build, with ownership of architecture, security and cost.
Build and maintain CI/CD pipelines with automated testing, safe database migrations and rollback.
Write infrastructure as code (Terraform or equivalent) held to the same review and versioning standards as application code.
Instrument services with metrics, structured logs and tracing so problems are visible before customers report them.
Own what you ship in production, including incident resolution and fixing root causes in code.

Architecture & Technical Leadership

Own architecture for backend services and set technical direction through design reviews, RFCs and working code rather than mandate.
Take ambiguous problems from definition through design, implementation and testing to production.
Document consequential technical decisions, including trade-offs and the options deliberately rejected.
Mentor engineers on backend design, distributed systems and production ownership, and unblock teams on hard technical problems.
Use Claude Code, Cursor and equivalent AI coding agents in daily work, and define the review standards for AI-assisted changes.

THE ENVIRONMENT

Technology Environment

Backend: Java, Spring Boot, Python, Django, FastAPI, Go.
Messaging & Streaming: Kafka, MQTT, Apache Flink and event-driven architectures.
Data: PostgreSQL, Redis and time-series databases.
Cloud: AWS.
Infrastructure: Docker, Kubernetes, Helm and Terraform.
CI/CD: GitHub Actions or equivalent modern CI/CD tooling.
Observability: Prometheus, Grafana, OpenTelemetry and Sentry.
APIs & Contracts: REST, OpenAPI and AsyncAPI.
Frontend: React and TypeScript (working knowledge).
AI Tooling: Claude Code, Cursor and LLM APIs.

WHAT WE'RE LOOKING FOR

Qualifications & Experience

Essential: Backend Engineering

8+ years of software engineering experience, the majority in hands-on backend development of production systems.
Deep expertise in Java/Spring Boot or Python (Django/FastAPI), writing production-quality code every week rather than only reviewing it.
Strong API design, data modelling and SQL skills, including schema and query optimisation.
Solid testing discipline, with unit, integration and contract tests as part of normal delivery.

Essential: Distributed Systems

Proven track record designing, building and running distributed systems in production at scale.
Hands-on experience with Kafka or equivalent messaging and event-driven architecture.
Strong grasp of concurrency, consistency, partitioning, idempotency and failure handling.
Exceptional debugging ability across service, database, messaging and network layers.

Essential: DevOps, AWS & Kubernetes

Hands-on production experience on AWS across compute, networking, storage, managed databases and IAM.
Strong Docker fundamentals and hands-on Kubernetes experience deploying, scaling and troubleshooting services in production.
CI/CD pipelines and infrastructure as code built and maintained first-hand rather than inherited.
Practical observability experience with metrics, logging, tracing and alerting.

Essential: Leadership & Ways of Working

Track record of technical influence beyond personal output: standards adopted, teams unblocked and engineers levelled up.
Ability to work independently with ambiguous requirements and bring clarity to others.
Strong written communication across design documents, RFCs, post-mortems and runbooks.
Bias towards owning outcomes rather than tickets, and towards fixing causes rather than symptoms.
Fluent use of Claude Code, Cursor or equivalent AI development tools in daily engineering work.

Desirable

IoT, energy, industrial or real-time data experience.
Apache Flink, MQTT or time-series databases (ClickHouse, InfluxDB) at scale.
Go in production.
React and TypeScript working knowledge.
Experience building LLM-powered or Agentic AI features in production.
Experience in high-velocity product or startup environments.

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