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.