Senior Backend Engineer | Founding Team

Job description

Title : Senior Backend Engineer | Founding Team

Location : Bengaluru / Hybrid

Experience : 6–10 years

Type : Full-time | Founding Team |

About DecisionX

www.decisionx.ai

We’re building a Self-Learning, Context-Aware Decision AI — one that uses Ontology, Context, and AI Agents to reimagine how humans and organisations make complex decisions. Our platform learns continuously, understands intent, and reasons across information — bringing clarity to the moments that matter most.

We’re a small team of serial founders, engineers, physicists, product artists, and builders who believe :

“The future will be shaped not by those with the most data, but by those who decide best.”

Our mission is bold yet simple — to build the last piece of Decision Intelligence humanity will ever need, empowering better choices for today’s world and for the futures yet to come.

Headquartered in Bangalore and San Francisco, DecisionX is led by a global team advancing the next frontier of Reasoning and Decision AI.

What You'll Build

Data Backbone

ETL / ELT pipelines for structured / unstructured data from ERP, APIs, warehouses, files

Data models for time-series, hierarchical dimensions, multi-billion row aggregations

OLAP query layers : sub-second performance on complex analytics

Streaming / batch ingestion with exactly-once semantics

Tech : ClickHouse, Druid, Snowflake, BigQuery, Redshift, Airflow, Kafka

Backend & APIs

Microservices architecture with clean boundaries and fault isolation

RESTful / GraphQL APIs that are simple to use, impossible to misuse

Async processing, job orchestration, distributed workflows

High availability, horizontal scale, graceful degradation

Tech : Python / Java / Go, Celery, Temporal, RabbitMQ, Redis, Kubernetes

Agent Orchestration

Multi-step agent workflows : goal decomposition, task coordination, learning loops

Stateful execution engines tracking decision context across sessions

Reasoning loops where agents query, simulate, and update plans autonomously

Abstractions for LLMs, analytical models, optimization solvers

Error recovery, retries, human-in-the-loop patterns

Numerical Analytics at Scale

High-volume, high-dimensional datasets : sales, financials, operational KPIs

Aggregation engines for dashboards, anomaly detection, RCA

Query optimization : indexing, materialized views, pre-aggregation, caching

Statistical / ML analytics : forecasting, outlier detection, correlation analysis

Numerical accuracy and consistency in distributed systems

You Must Have

Backend :

6–10 years building distributed systems in production

Expert in Python, Java, or Go

Microservices, API design, async processing (Celery, Kafka, RabbitMQ)

Distributed systems : consistency, partitioning, replication, fault tolerance

Data :

ETL / ELT pipelines processing millions of records daily

Data warehouses / OLAP : Snowflake, Redshift, BigQuery, ClickHouse, or Druid

Strong SQL, query optimization, data modeling (star schemas, time-series)

Orchestration tools : Airflow, Prefect, or Dagster

Quantitative :

Large-scale numerical datasets : financials, metrics, time-series

Aggregation strategies, rollups, pre-computation

Numerical precision and correctness in distributed systems

Infrastructure :

AWS, GCP, or Azure in production

Docker, CI / CD, infrastructure-as-code

Monitoring / logging / alerting : Prometheus, Grafana, Datadog

Mindset :

You've debugged distributed systems at 3 AM and shipped the fix

You understand trade-offs : ClickHouse vs. Snowflake, when to denormalize, when to cache

You write code other engineers want to read

Bonus :

Worked in the domain of BI platforms, dashboards, analytics engines before

Agentic AI, LLM orchestration, reasoning workflows excite you

Startup / founding team experience in the past

Why Join

Founding Team : Meaningful equity, outsized influence on architecture and culture, direct founder collaboration.

Technical Leverage : Build systems powering Fortune 500 decision-making. Deep technical work = defensible moats.

Domain : Enterprise decision intelligence is infrastructure, not a feature. You'll work at the intersection of distributed systems, data engineering, agentic AI, and decision science.

Ownership : This isn't a job. It's a chance to build something that matters and own a piece of it.