
š¤ Data Scientist ā Frontier AI for Data Platforms & Distributed Systems (4ā8 Years)
Experience: 4ā8 Years
Location: Bengaluru (On-site / Hybrid)
Company: Publicly Listed, Global Product Platform
š§ About the Mission
We are building a Top 1% AI-Native Engineering & Data Organization ā from first principles.
This is not incremental improvement.
This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.
We are re-architecting:
- Legacy systems ā AI-native architectures
- Static pipelines ā autonomous, self-healing systems
- Data platforms ā intelligent, learning systems
- Software workflows ā agentic execution layers
This is the kind of shift you would expect from companies like Google or Microsoft ā
Except here, you will build it from day zero and scale it globally.
š§ The Opportunity: This role sits at the intersection of three high-impact domains:
1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI
2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines
3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure
We are building systems where:
- Data platforms optimize themselves using ML/LLMs
- Pipelines are autonomous, self-healing, and adaptive
- Queries are generated, optimized, and executed intelligently
- Infrastructure learns from usage and evolves continuously
This is: AI as the control plane for data infrastructure
š§© What Youāll Work On
You will design and build AI-native systems deeply embedded inside data infrastructure.
1. AI-Native Data Platforms
- Build LLM-powered interfaces:
- Natural language ā SQL / pipelines / transformations
- Design semantic data layers:
- Embeddings, vector search, knowledge graphs
- Develop AI copilots:
- For data engineers, analysts, and platform users
2. Autonomous Data Pipelines
- Build self-healing ETL/ELT systems using AI agents
- Create pipelines that:
- Detect anomalies in real time
- Automatically debug failures
- Dynamically optimize transformations
3. Intelligent Query & Compute Optimization
- Apply ML/LLMs to:
- Query planning and execution
- Cost-based optimization using learned models
- Workload prediction and scheduling
- Build systems that:
- Learn from query patterns
- Continuously improve performance and cost efficiency
4. Distributed Data + AI Infrastructure
- Architect systems operating at:
- Billions of events per day
- Petabyte-scale data
- Work with:
- Distributed compute engines (Spark / Flink / Ray class systems)
- Streaming systems (Kafka-class infra)
- Vector databases and hybrid retrieval systems
5. Learning Systems & Feedback Loops
- Build closed-loop AI systems:
- Execution ā feedback ā model updates
- Develop:
- Continual learning pipelines
- Online learning systems for infra optimization
- Experimentation frameworks (A/B, bandits, eval pipelines)
6. LLM & Agentic Systems (Infra-Aware)
- Build agents that understand data systems
- Enable:
- Autonomous pipeline debugging
- Root cause analysis for infra failures
- Intelligent orchestration of data workflows
š§ What Weāre Looking For
Core Foundations
- Strong grounding in:
- Machine Learning, Deep Learning, NLP
- Statistics, optimization, probabilistic systems
- Distributed systems fundamentals
- Deep understanding of:
- Transformer architectures
- Modern LLM ecosystems
Hands-On Expertise
- Experience building:
- LLM / GenAI systems (RAG, fine-tuning, embeddings)
- Data platforms (warehouse, lake, lakehouse architectures)
- Distributed pipelines and compute systems
- Strong programming skills:
- Python (ML/AI stack)
- SQL (deep understanding ā query planning, optimization mindset)
Systems Thinking (Critical)
You think in systems, not components.
- Built or worked on:
- Large-scale data pipelines
- High-throughput distributed systems
- Low-latency, high-concurrency architectures
- Understand:
- Query optimization and execution
- Data partitioning, indexing, caching
- Trade-offs in distributed systems
š„ What Sets You Apart (Top 1%)
- Built AI-powered data platforms or infra systems in production
- Designed or contributed to:
- Query engines / optimizers
- Data observability / lineage systems
- AI-driven infra or AIOps platforms
- Experience with:
- Multi-modal AI (logs, metrics, traces, text)
- Agentic AI systems
- Autonomous infrastructure
- Worked on systems at scale comparable to:
- Google (BigQuery-like systems)
- Meta (real-time analytics infra)
- Snowflake / Databricks (lakehouse architectures)
𧬠Ideal Background (Not Mandatory)
We often see strong candidates from:
- Data infrastructure or platform engineering teams
- AI-first startups or research-driven environments
- High-scale product companies
Experience building:
- Internal platforms used by 1000s of engineers
- Systems serving millions of users / high throughput workloads
- Multi-region, distributed cloud systems
š§ The Kind of Problems Youāll Solve
- Can LLMs replace traditional query optimizers?
- How do we build self-healing data pipelines at scale?
- Can data systems learn from every query and improve automatically?
- How do we embed reasoning and planning into infrastructure layers?
- What does a fully autonomous data platform look like?
Background: We Commonly See (But Not Limited To)
Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:
- Leading engineering schools in India and globally
- Engineers with experience in top product companies, AI startups, or research-driven environments
- That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

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