Data Scientist

Public Listed - Product Based company

Data Scientist

Recruiting Bond

Company

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Data Scientist

at Public Listed - Product Based company

Agency job

via Recruiting Bond

4 - 8 yrs

₹25L - ₹70L / yr

Bengaluru (Bangalore)

Skills

Data Science

data platforms

Data-flow analysis

Data pipelines

AI Infrastructure

Large Language Models (LLM)

Large Language Models (LLM) tuning

Agentic Systems

AI Agents

Machine Learning (ML)

Deep Learning

Natural Language Processing (NLP)

Statistical Modeling

Gen AI

Retrieval Augmented Generation (RAG)

Data Warehouse (DWH)

Data modeling

Python

PyTorch

AI/ML

Multi-modal AI

SQL

PostgreSQL

MongoDB

Query planning

Prompt engineering

ETL

ELT

AI copilots

Generative AI

Artificial Intelligence (AI)

Google Vertex AI

Vector database

🤖 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

🔥 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.

🤖 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

🔥 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.

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Full Stack Developer - Averlon

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