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.

Users love Cutshort

Read about what our users have to say about finding their next opportunity on Cutshort.

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.

Companies hiring on Cutshort

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Lead - Cloud Security and Reliability Engineer

at Searce Inc

3 recruiters

Posted by Mohammed Rabidheen

Coimbatore

5 - 10 yrs

Best in industry

Microsoft Windows Azure

Kubernetes

Terraform

Observability

Reliability engineering

About Searce

Searce (pronounced 'search') is a global, AI-native, and engineering-led modern technology consultancy. Founded in 2004 with a vision to "solve for better," we partner with organizations to "futurify" their businesses by leveraging the full power of Cloud, AI, and Data Engineering.

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The "Futurify" DNA: We don't just maintain; we improve. We use our proprietary EVLOS business innovation framework to ensure our clients aren't just moving to the cloud, but are staying ahead of the curve.

Our Culture: The HAPPIER Values

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Humble: We learn from everyone.
Adaptable: We embrace change as the only constant.
Positive: We focus on solutions, not just problems.
Passionate: We are obsessed with engineering excellence.
Innovative: We challenge the status quo.
Excellence: We deliver impactful, futuristic outcomes.
Responsible: We take ownership of our work and its impact.

Your Mission: The Role

solving for better.

You are a reliability-owning, hands-on solver. Not just a "break-fix engineer."

As a DRI (directly responsible individual) for our clients' most critical systems, you’ll be the go-to expert within the squad that ensures their environments are secure, reliable, and optimized 24/7. You will deliver measurable impact – improved uptime, faster response times, and real cost savings. Not just closed tickets. Not just alerts. Real outcomes you engineer yourself.

You will lead the charge on technical execution, from complex troubleshooting and root cause analysis to engineering proactive, automated solutions. This role is about building the future of reliable cloud operations and shipping it into today's production environments.

Your Responsibilities

what you will wake up to solve.

This isn’t a “manage tickets” role. You are the architect, the executioner and the DRI for our Cloud Managed Services GTM, deploying solutions that turn operational noise into hardened outcomes. Here’s how you’ll make your mark:

Own Service Reliability: You will be the go-to technical expert for 24/7 cloud operations and incident management. You'll ensure strict adherence to SLOs by getting your hands dirty, leading high-stakes troubleshooting to deliver a superior client experience.
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Automate with Intelligence: You'll lead the charge from the keyboard to futurify our operations. You'll embed AI-driven automation, predictive monitoring, and AIOps into core processes to eliminate toil and preempt incidents.
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Service Delivery Mindset: A deep understanding of MSP business models, SLAs, and the importance of client satisfaction in an operational context.
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About Searce

Why Join the "Solvers" Brigade?

Award-Winning Excellence: In 2026, Searce was recognized as the Google Cloud Workplace AI Transformation Partner of the Year (APAC). We are a Premier Google Cloud Partner and a top-tier Managed Services Provider (MSP).
AI-First Mindset: We specialize in Applied AI (Generative & Conventional), Cloud Modernization, and Location Intelligence, helping industries from FinServ and Healthcare to Retail and Manufacturing reinvent themselves.
The "Futurify" DNA: We don't just maintain; we improve. We use our proprietary EVLOS business innovation framework to ensure our clients aren't just moving to the cloud, but are staying ahead of the curve.

Our Culture: The HAPPIER Values

We look for individuals who live and breathe our HAPPIER values:

Humble: We learn from everyone.
Adaptable: We embrace change as the only constant.
Positive: We focus on solutions, not just problems.
Passionate: We are obsessed with engineering excellence.
Innovative: We challenge the status quo.
Excellence: We deliver impactful, futuristic outcomes.
Responsible: We take ownership of our work and its impact.

Your Mission: The Role

solving for better.

You are a reliability-owning, hands-on solver. Not just a "break-fix engineer."

Your Responsibilities

what you will wake up to solve.

Own Service Reliability: You will be the go-to technical expert for 24/7 cloud operations and incident management. You'll ensure strict adherence to SLOs by getting your hands dirty, leading high-stakes troubleshooting to deliver a superior client experience.
Engineer the Blueprint: You'll translate client needs into scalable, automated, and secure cloud architectures. You will write and maintain the operational playbooks and Infrastructure as Code (IaC) that your squad uses every day.
Automate with Intelligence: You'll lead the charge from the keyboard to futurify our operations. You'll embed AI-driven automation, predictive monitoring, and AIOps into core processes to eliminate toil and preempt incidents.
Drive FinOps & Impact: You'll own the technical execution of the FinOps framework. You will continuously analyze, configure, and optimize cloud spend for clients through hands-on engineering.
Be the Expert in the Room: You'll share your knowledge through internal demos, documentation, and technical deep dives, representing the deep expertise that turns operational complexity into business resilience.
Mentor & Elevate: You will be a technical mentor for your peers. Through code reviews and collaborative problem-solving, you'll help build a high-performing squad that lives the “Always Hardened” mindset.

Experience & Relevance

We are looking for future technology leaders, not just coders. We value raw intelligence, analytical rigor, and an obsessive passion for technology over any prior experience.

Cloud Operations Pedigree: 5+ years of experience in Azure cloud infrastructure, with a significant portion in cloud managed services. Hands-on experience in Kubernetes is mandatory.
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Service Delivery Mindset: A deep understanding of MSP business models, SLAs, and the importance of client satisfaction in an operational context.
Client Engagement: Ability to ask appropriate questions to get to the heart of an operational issue and win trust with stakeholders.
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Sr. PPC Specialist

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1 recruiter

Posted by Dhwani Saija

Ahmedabad

3 - 6 yrs

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What You’ll Do:

• Plan, create, and manage PPC campaigns on Google Ads, Facebook, and Instagram.

• Handle ad budgets of $10,000+ per month across search, display, and video campaigns.

• Develop strategies to improve ROI, conversion rates, and overall campaign efficiency.

• Create and test compelling ad copy, visuals, and targeting to maximise results.

• Build retargeting and eCommerce campaigns that drive real conversions.

• Analyse data in GA4 and prepare reports using Looker Studio.

• Regularly A/B test landing pages, audiences, and creative to find what works best.

• Collaborate with cross-functional teams to align ad performance with business goals.

• Prepare campaign proposals and pricing strategies for new clients.

What You Bring:

• 5+ years of hands-on experience in PPC campaign management.

• Strong understanding of Google Ads, Meta Ads, and other paid media platforms.

• Experience managing campaigns for international clients (US market experience is a plus).

• Solid grasp of data analysis and ability to turn insights into actions.

• Excellent communication skills — written and verbal.

• A proactive mindset that you enjoy testing, learning, and improving every day.

• Bonus: Copywriting skills and an eye for creativity that converts.

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