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Data Scientist
Public Listed - Product Based company
Public Listed - Product Based company's logo

Data Scientist

at Public Listed - Product Based company

Agency job
4 - 8 yrs
₹25L - ₹70L / yr
Bengaluru (Bangalore)
Skills
skill iconData Science
data platforms
Data-flow analysis
Data pipelines
AI Infrastructure
Large Language Models (LLM)
Large Language Models (LLM) tuning
Agentic Systems
AI Agents
skill iconMachine Learning (ML)
skill iconDeep Learning
Natural Language Processing (NLP)
Statistical Modeling
Gen AI
Retrieval Augmented Generation (RAG)
Data Warehouse (DWH)
Data modeling
skill iconPython
PyTorch
AI/ML
Multi-modal AI
SQL
skill iconPostgreSQL
skill iconMongoDB
Query planning
Prompt engineering
ETL
ELT
AI copilots
Generative AI
Artificial Intelligence (AI)
Google Vertex AI
Vector database

šŸ¤– Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform


🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — 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 → AI-native architectures
  • Static pipelines → autonomous, self-healing systems
  • Data platforms → intelligent, learning systems
  • Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.


🧠 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


🧩 What You’ll 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 → 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 → feedback → 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


🧠 What We’re 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 — 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


šŸ”„ 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)


🧬 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


🧠 The Kind of Problems You’ll 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.


Read more
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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