Reach out to Decision Makers (B2b) / Connect with the relevant individuals (as per defined database) to brief them on call about upcoming conferences, sessions, etc. Educate them why they should attend the conference/event and generate interest.
Work on Business-to-Business campaign for the IT marketplace
Doing Web search for successful call results ie. LinkedIn
Guide and help the participants in the process like registering, logging-in on the portal by carrying out the intelligent conversation, sharing appropriate information with registrants
Maintaining and analyzing reports of discussions and call history as required in a specific format.
Making polite conversation with customers/prospects on the phone

Job Description/Job Responsibilities

Reach out to Decision Makers (B2b) / Connect with the relevant individuals (as per defined database) to brief them on call about upcoming conferences, sessions, etc. Educate them why they should attend the conference/event and generate interest.
Work on Business-to-Business campaign for the IT marketplace
Doing Web search for successful call results ie. LinkedIn
Guide and help the participants in the process like registering, logging-in on the portal by carrying out the intelligent conversation, sharing appropriate information with registrants
Maintaining and analyzing reports of discussions and call history as required in a specific format.
Making polite conversation with customers/prospects on the phone

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About Kestone Integrated Marketing Services

Founded :

1997

Type :

Products & Services

Size :

100-1000

Stage :

Profitable

About

Creative. Innovative. Imaginative. We are in the business of delivering Integrated Sales & Marketing campaigns.

Veena S

Connect

Maria Anjali

Connect

Company social profiles

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Role: Principal Engineer, Salesforce Health cloud
Experience: 9-11 Years
Employment Type: Full-time
Work Mode: Remote

REQUIREMENTS:

Should be able to lead end-to-end Salesforce Health Cloud solution architecture and delivery for large, enterprise healthcare implementations.
Deep expertise in Health Cloud features, including Patient/Member Profiles, Care Plans, Care Programs, Care Teams, Health Timelines, Assessments, Care Gaps, and Utilization Management.
Strong leadership in translating complex healthcare business requirements into scalable Salesforce Health Cloud solutions.
Should be able to drive architectural decisions covering Salesforce core, Health Cloud extensions, integrations, data models, and automation.
Should have hands-on leadership experience with Apex, Lightning Web Components (LWC), Flows, Triggers, Batch Apex, and Salesforce APIs.
Should be able to define and govern integration architecture using REST/SOAP APIs, middleware, and healthcare standards such as FHIR and HL7.
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Should be able to lead all phases of the delivery lifecycle requirements, solution design, development, testing, deployment, and post-go-live support.
Provide technical governance, conduct design/code reviews, and manage architectural risks and technical debt.
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Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
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Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.
Own overall Health Cloud technical strategy, ensuring scalability, security, and compliance with healthcare regulations (HIPAA/PHI).Showcasing a consulting mindset by acting as a solution provider rather than an order taker.
Act as a trusted advisor for business stakeholders, architects, and senior leadership on Health Cloud capabilities and best practices.
Review and approve high-level and low-level technical designs, ensuring alignment with enterprise architecture standards.
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Qualifications

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Job Details:

Role: Principal Engineer, Salesforce Health cloud
Experience: 9-11 Years
Employment Type: Full-time
Work Mode: Remote

REQUIREMENTS:

Should be able to lead end-to-end Salesforce Health Cloud solution architecture and delivery for large, enterprise healthcare implementations.
Deep expertise in Health Cloud features, including Patient/Member Profiles, Care Plans, Care Programs, Care Teams, Health Timelines, Assessments, Care Gaps, and Utilization Management.
Strong leadership in translating complex healthcare business requirements into scalable Salesforce Health Cloud solutions.
Should be able to drive architectural decisions covering Salesforce core, Health Cloud extensions, integrations, data models, and automation.
Should have hands-on leadership experience with Apex, Lightning Web Components (LWC), Flows, Triggers, Batch Apex, and Salesforce APIs.
Should be able to define and govern integration architecture using REST/SOAP APIs, middleware, and healthcare standards such as FHIR and HL7.
Must have strong understanding of EHR/EMR integrations, payer/provider workflows, and patient/member lifecycle management.
Should be able to lead all phases of the delivery lifecycle requirements, solution design, development, testing, deployment, and post-go-live support.
Provide technical governance, conduct design/code reviews, and manage architectural risks and technical debt.
Should be able to lead and mentor multi-shore Salesforce teams, including developers, admins, QA, and offshore delivery teams.
Should have hands-on experience with DevOps and CI/CD tools such as Copado, Git, Azure DevOps, Jenkins.
Should be able to collaborate closely with program managers to manage scope, timelines, risks, and delivery commitments.
Should be able to drive innovation and continuous improvement, including PoCs, adoption of new Salesforce Health Cloud features, and platform enhancements.
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Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers
Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
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🤖 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.

UI/UX Designer

at grootan

Posted by ezto io

Chennai

1 - 2 yrs

₹4L - ₹5L / yr

canva

User Interface (UI) Design

User Experience (UX) Design

Adobe XD

Adobe Illustrator

+2 more

Job highlights

-Conduct user research and analysis to inform design decisions and enhance the overall user experience

-The successful candidate should have a keen understanding of current design trends, usability principles, and a strong portfolio showcasing their skills.

-Proficient in UI / UX design principles and best practices

Summary: We are seeking a talented and creative Web Designer with 1- 2 years of experience and expertise in both User Interface (UI) and User Experience (UX) design. The ideal candidate will be responsible for designing visually appealing and user-friendly websites that effectively meet the needs of our clients and end-users. The successful candidate should have a keen understanding of current design trends, usability principles, and a strong portfolio showcasing their skills.

Roles and Responsibilities:

-Create visually stunning and intuitive user interfaces for websites and web applications.

-Develop wireframes, prototypes, and mockups to effectively communicate design ideas.

-Ensure a seamless and engaging user experience through thoughtful and user-centric design.

-Work closely with cross-functional teams, including developers and product managers, to understand project requirements and objectives.

-Collaborate with stakeholders to gather and incorporate feedback, ensuring alignment with project goals.

-Stay informed about industry trends, design best practices, and emerging technologies.

-Conduct user research and analysis to inform design decisions and enhance the overall user experience.

-Implement responsive design principles to ensure optimal user experiences across various devices and screen sizes.

-Create interactive prototypes for user testing and feedback.

-Conduct usability and accessibility testing and gather insights to refine and improve designs.

-Develop and optimize graphics for use on websites, ensuring a cohesive and visually appealing design.

Requirement-

- Bachelor's degree /Certified course in UI/UX Design

- Proven experience in UI/UX design with a strong portfolio showcasing relevant work.

-Familiarity with industry-standard design tools such as Adobe Creative Suite, Canva, Sketch, Figma, etc.