Data Engineering Internship

Dataweave Pvt Ltd is hiring Data Engineering Internship job in Bengaluru (Bangalore) | Cutshort

Data Engineering Internship
Data Engineering Internship

0
\- 1 yrs
Best in industry
Bengaluru (Bangalore)
Skills
Data engineering
Internship
Python
Looking for the Candiadtes , good in coding
scraping , and problem skills
Read more
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

Founded
:
2011
Type
:
Products & Services
Size
:
100-1000
Stage
:
Raised funding
About
The company provides market intelligence solutions to retailers and brands by aggregating and visualizing data from the web, helping them monitor and analyze public data through APIs, dashboards, and visualizations. Its products include Retail Intelligence and Brand Analytics, which offer promotional insights, pricing intelligence, assortment and gap analytics, catalog insights, price monitoring, seller analytics, and momentum analytics. The company has entered into a strategic partnership with Aucfan to expand its presence in the Japanese market and has raised an undisclosed amount in a Series A funding round led by FreakOut Group and WaterBridge Ventures.
Read more
Connect with the team
Company social profiles

Similar jobs

Posted by
Akshay Patil
Remote only
10
\- 15 yrs
₹10L - ₹18L / yr
Solution architecture
Denodo
Data Virtualization
Data architecture
SQL
+5 more
**_Job Title :_** _Solution Architect – Denodo_
**_Experience :_** _10+ Years_
**_Location :_** _Remote / Work from Home_
**_Notice Period :_** _Immediate joiners preferred_
**_Job Overview :_**
_We are looking for an experienced_ **_Solution Architect – Denodo_** _to lead the design and implementation of data virtualization solutions. In this role, you will work closely with cross-functional teams to ensure our data architecture aligns with strategic business goals. The ideal candidate will bring deep expertise in Denodo, strong technical leadership, and a passion for driving data-driven decisions._
**_Mandatory Skills :_** _Denodo, Data Virtualization, Data Architecture, SQL, Data Modeling, ETL, Data Integration, Performance Optimization, Communication Skills._
**_Key Responsibilities :_**
– _Architect and design scalable data virtualization solutions using_ **_Denodo_**_._
– _Collaborate with business analysts and engineering teams to understand requirements and define technical specifications._
– _Ensure adherence to best practices in_ **_data governance, performance, and security_**_._
– _Integrate_ **_Denodo_** _with diverse data sources and optimize system performance._
– _Mentor and train team members on_ **_Denodo_** _platform capabilities._
– _Lead tool evaluations and recommend suitable data integration technologies._
– _Stay updated with emerging trends in data virtualization and integration._
**_Required Qualifications :_**
– _Bachelor’s degree in Computer Science, IT, or a related field._
– **_10+ Years of experience_** _in_ **_data architecture_** _and_ **_integration._**
– _Proven expertise in_ **_Denodo_** _and_ **_data virtualization frameworks._**
– _Strong proficiency in_ **_SQL_** _and_ **_data modeling_**_._
– _Hands-on experience with_ **_ETL processes_** _and_ **_data integration tools._**
– _Excellent communication, presentation, and stakeholder management skills._
– _Ability to lead technical discussions and influence architectural decisions._
– _Denodo or data architecture certifications are a strong plus._
Read more

Posted by
Faisal AshrafNomani
Bengaluru (Bangalore), Chennai
4
\- 10 yrs
Best in industry
AWS
Windows Azure
Google Cloud Platform (GCP)
Large Language Models (LLM)
AI Agents
+2 more
Job Description:
We are seeking a Cloud & AI Platform Engineer to design and operate AI-native infrastructure that supports large-scale machine learning, generative AI, and agentic AI systems.
This role will focus on building secure, scalable, and automated multi-cloud platforms across AWS, Azure, GCP, and hybrid on-prem environments, enabling teams to deploy LLMs, AI agents, and data-driven applications reliably in production.
You will work at the intersection of cloud engineering, MLOps, LLMOps, DevOps, and data infrastructure, helping build platforms that support RAG pipelines, vector search, AI model lifecycle management, and AI observability.
Key Responsibilities
AI & Agentic Infrastructure
– Design infrastructure to support agentic AI systems, autonomous agents, and multi-agent workflows.
– Build scalable runtime environments for LLM orchestration frameworks.
– Enable deployment of AI copilots, assistants, and autonomous decision systems.
Common frameworks may include:
– LangChain
– LlamaIndex
– AutoGPT
LLMOps & AI Model Lifecycle
Design and manage LLMOps pipelines for the full lifecycle of large language models:
– Model deployment
– Prompt management
– Versioning
– Evaluation and testing
– Model monitoring
Integrate with AI platforms such as:
– Azure Machine Learning
– Amazon SageMaker
– Vertex AI
Retrieval-Augmented Generation (RAG) Infrastructure
Design and optimize RAG pipelines that integrate enterprise knowledge with LLMs.
Responsibilities include:
– Document ingestion pipelines
– Embedding generation workflows
– Knowledge indexing
– Query orchestration
– Retrieval optimization
– Support scalable semantic search architectures.
Vector Database & Knowledge Infrastructure
Deploy and manage vector databases used for AI applications and semantic retrieval.
Common technologies include:
– Pinecone
– Weaviate
– Milvus
– FAISS
Responsibilities include:
– Index optimization
– Query latency tuning
– Scalable embedding storage
– Hybrid search architecture
Multi-Cloud AI Infrastructure
Design and maintain AI-ready infrastructure across:
– Amazon Web Services
– Microsoft Azure
– Google Cloud Platform
Key responsibilities include:
– GPU infrastructure management
– Distributed training environments
– Hybrid cloud integrations with on-prem data centers
– Infrastructure scaling for AI workloads
Data Platforms & Integration
– Support deployment and optimization of data lakes, data warehouses, and streaming platforms.
– Work with data engineering teams to ensure secure and scalable data infrastructure.
Cloud Architecture & Infrastructure
– Design and implement scalable multi-cloud infrastructure across Azure, AWS, and Google Cloud.
– Build hybrid cloud architectures integrating on-premise environments with cloud platforms.
– Implement high availability, disaster recovery, and auto-scaling architectures for AI workloads.
DevOps, Platform Engineering & Automation
Build automated cloud infrastructure using modern DevOps practices.
Tools may include:
– Terraform
– Docker
– Kubernetes
– GitHub Actions
Responsibilities include:
– Infrastructure as Code (IaC)
– Automated deployments
– CI/CD pipelines for AI models and services
– Platform reliability and scalability
AI Observability & Monitoring
Implement observability frameworks to monitor AI systems in production.
This includes:
– Model performance monitoring
– Prompt evaluation
– Hallucination detection
– Latency and throughput analysis
– Cost monitoring for LLM usage
Tools may include:
– Arize AI
– WhyLabs
– Weights & Biases
Security, Governance & Responsible AI
Ensure AI systems follow strong governance and security practices.
Responsibilities include:
– Data privacy and compliance
– Model governance frameworks
– Secure model deployment
– Monitoring model bias and drift
– AI risk management
Support enterprise frameworks for Responsible AI and AI compliance.
Data & Security
– Experience with data lake architectures, distributed storage, and ETL pipelines
– Knowledge of data security, encryption, IAM, and compliance frameworks
– Familiarity with AI governance and responsible AI practices
Required Skills
Cloud & Infrastructure
– Strong experience in Azure (must have), AWS or GCP
– Hybrid and multi-cloud architecture
– GPU infrastructure management
DevOps & Automation
– Kubernetes
– Docker
– Terraform
– CI/CD pipelines
AI / ML Platforms
– MLOps pipelines
– Model deployment
– Model monitoring
AI Application Infrastructure
– Vector databases
– RAG pipelines
– LLM orchestration frameworks
Programming
Experience in one or more languages:
– Python
– Go
– Java
– TypeScript
Preferred Qualifications
– Experience building AI copilots or autonomous agents
– Knowledge of distributed model training - Knowledge of GPU infrastructure and distributed training
– Familiarity with AI evaluation frameworks - Familiarity with model monitoring, drift detection, and AI observability
– Experience building enterprise AI platforms
Education & Experience
– Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
– 4–8+ years experience in cloud infrastructure, DevOps, or platform engineering
– Experience working in data-driven or AI-focused environments
What Success Looks Like
– Reliable ML model deployment pipelines - Reliable infrastructure for LLMs and AI agents, Scalable RAG knowledge platforms
– Effic