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UI / UX Designer for Industrial IOT Product.

User-centered design principles: Deep understanding of user needs and behaviors.

Information architecture: Organizing and structuring content for optimal user experience.

Interaction design: Creating intuitive and engaging user flows and interactions.

Wireframing & Prototyping: Creating low and high-fidelity prototypes to test and iterate designs.

Analytics & data analysis: Using data to inform design decisions and measure design effectiveness.

Visual design principles: Typography, color theory, layout, and visual hierarchy.

UI design tools: Figma, Sketch, Adobe XD, etc.

Responsive design: Designing interfaces that adapt seamlessly to various screen sizes.

Accessibility standards: WCAG guidelines for creating inclusive designs.

Front-end development basics: HTML, CSS, and JavaScript for understanding technical feasibility.

Software & Tools:

Design software: Figma, Sketch, Adobe Creative Suite (Photoshop, Illustrator), InVision, etc.

Prototyping tools: Figma, Adobe XD, InVision Studio, Axure, etc.

Collaboration tools: Jira, Confluence, Slack, etc.

Version control: Git or similar for managing design files.

Soft Skills:

Communication: Clearly articulating design decisions and presenting ideas to stakeholders.

Collaboration: Working effectively with cross-functional teams (developers, product managers, marketers).

Problem-solving: Identifying user pain points and developing creative solutions.

Critical thinking: Analyzing user data and feedback to make informed design decisions.

Mentorship: Guiding and supporting junior designers.

Time management: Handling multiple projects and meeting deadlines.

Adaptability: Staying current with design trends and emerging technologies.

Beyond the Basics:

Domain expertise: Industrial IOT is a plus

SENIOR DATA ENGINEER:

ROLE SUMMARY:

Own the design and delivery of petabyte-scale data platforms and pipelines across AWS and modern Lakehouse stacks. You’ll architect, code, test, optimize, and operate ingestion, transformation, storage, and serving layers. This role requires autonomy, strong engineering judgment, and partnership with project managers, infrastructure teams, testers, and customer architects to land secure, cost-efficient, and high-performing solutions.

RESPONSIBILITIES:

• Architecture and design: Create HLD/LLD/SAD, source–target mappings, data contracts, and optimal designs aligned to requirements.
• Pipeline development: Build and test robust ETL/ELT for batch, micro-batch, and streaming across RDBMS, flat files, APIs, and event sources.
• Performance and cost tuning: Profile and optimize jobs, right-size infrastructure, and model license/compute/storage costs.
• Data modeling and storage: Design schemas and SCD strategies; manage relational, NoSQL, data lakes, Delta Lakes, and Lakehouse tables.
• DevOps and release: Establish coding standards, templates, CI/CD, configuration management, and monitored release processes.
• Quality and reliability: Define DQ rules and lineage; implement SLA tracking, failure detection, RCA, and proactive defect mitigation.
• Security and governance: Enforce IAM best practices, retention, audit/compliance; implement PII detection and masking.
• Orchestration: Schedule and govern pipelines with Airflow and serverless event-driven patterns.
• Stakeholder collaboration: Clarify requirements, present design options, conduct demos, and finalize architectures with customer teams.
• Leadership: Mentor engineers, set FAST goals, drive upskilling and certifications, and support module delivery and sprint planning.

REQUIRED QUALIFICATIONS:

• Experience: 15+ years designing distributed systems at petabyte scale; 10+ years building data lakes and multi-source ingestion.
•  Cloud (AWS): IAM, VPC, EC2, EKS/ECS, S3, RDS, DMS, Lambda, CloudWatch, CloudFormation, CloudTrail.
• Programming: Python (preferred), PySpark, SQL for analytics, window functions, and performance tuning.
• ETL tools: AWS Glue, Informatica, Databricks, GCP DataProc; orchestration with Airflow.
• Lakehouse/warehousing: Snowflake, BigQuery, Delta Lake/Lakehouse; schema design, partitioning, clustering, performance optimization.
• DevOps/IaC: Terraform with 15+ years of practice; CI/CD (GitHub Actions, Jenkins) with 10+ years; config governance and release management.
• Serverless and events: Design event-driven distributed systems on AWS.
• NoSQL: 2–3 years with DocumentDB including data modeling and performance considerations.
• AI services: AWS Entity Resolution, AWS Comprehend; run custom LLMs on Amazon SageMaker; use LLMs for PII classification.

NICE-TO-HAVE QUALIFICATIONS:

• Data governance automation: 10+ years defining audit, compliance, retention standards and automating governance workflows.
• Table and file formats: Apache Parquet; Apache Iceberg as analytical table format.
• Advanced LLM workflows: RAG and agentic patterns over proprietary data; re-ranking with index/vector store results.
• Multi-cloud exposure: Azure ADF/ADLS, GCP Dataflow/DataProc; FinOps practices for cross-cloud cost control.

OUTCOMES AND MEASURES:

• Engineering excellence: Adherence to processes, standards, and SLAs; reduced defects and non-compliance; fewer recurring issues.
• Efficiency: Faster run times and lower resource consumption with documented cost models and performance baselines.
• Operational reliability: Faster detection, response, and resolution of failures; quick turnaround on production bugs; strong release success.
• Data quality and security: High DQ pass rates, robust lineage, minimal security incidents, and audit readiness.
• Team and customer impact: On-time milestones, clear communication, effective demos, improved satisfaction, and completed certifications/training.

LOCATION AND SCHEDULE:

●      Location: Outside US (OUS).

●      Schedule: Minimum 6 hours of overlap with US time zones.

Job Description:

We are looking for a proactive Business Development Executive with experience in B2B sales to drive growth in our e-commerce operations. The ideal candidate will focus on acquiring new business clients, managing vendor relationships, and supporting sales through digital marketing initiatives.

Key Responsibilities:

• Generate and convert B2B leads for e-commerce partnerships
• Onboard and manage vendors/suppliers on the platform
• Build and maintain strong client relationships
• Support digital marketing campaigns to boost product visibility and sales
• Monitor market trends and competitor activities
• Achieve monthly sales and business targets

Key Skills:

• B2B sales and client acquisition
• Knowledge of e-commerce operations
• Understanding of digital marketing (SEO, SEM, social media)
• Strong communication and negotiation skills
• CRM and lead management tools proficiency
• Self-motivated and target-driven

Name of the College: KGiSL Institute of Technology

College Profile: The main objective of KGiSL Institute of Technology is to provide industry embedded education and to mold the students for leadership in industry, government, and educational institutions; to advance the knowledge base of the engineering professions; and to influence the future directions of engineering education and practice. The ability to connect to the future challenges and deliver industry-ready human resources is a credibility that KGISL Educational Institutions have progressively excelled at. Industry -readiness of its students is what will eventually elevate an institution to star status and its competitiveness in the job market. Choice of such an institution will depend on its proximity to industry, the relevance of its learning programme to real-time industry and the active connect that a student will have with industry professionals.

Job Title: Assistant Professor

Departments:

·      S&H (Mathematics) - Assistant Professor

Qualification: 

·      B.sc, M.sc & Ph.D.

Immediate Joiner preferred

Candidate Profile:

·      Candidate should complete their PhD in the relevant discipline

·      Candidate should have Good Communication Skills

·      Commitment towards work

We are looking for "Mulesoft Developer" for Reputed Client @ Coimbatore Permanent Role.

• 5 Years of IT experience

• Current 3+ years of hands-on MuleSoft development experience, including creating custom Mule

• Enterprise-level Java development experience (ex. J2EE web development experience using but not limited to EJB, Servlet, JDBC, ORM, etc.)

• Experience with SOAP and REST Web services, RAML Swagger open API development, and SOA APIs ensuring MuleSoft best practices.

• Experience with concepts for MuleSoft implementations including encryption, security, logging, throttling, scalability and clustering.

• A pro-active individual with strong communication skills

We are looking for a highly capable machine learning engineer to optimize our machine learning systems. You will be evaluating existing machine learning (ML) processes, performing statistical analysis to resolve data set problems, and enhancing the accuracy of our AI software's predictive automation capabilities.

To ensure success as a machine learning engineer, you should demonstrate solid data science knowledge and experience in a related ML role. A first-class machine learning engineer will be someone whose expertise translates into the enhanced performance of predictive automation software.

Machine Learning Engineer Responsibilities:

Consulting with managers to determine and refine machine learning objectives.

Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.

Transforming data science prototypes and applying appropriate ML algorithms and tools.

Ensuring that algorithms generate accurate user recommendations.

Turning unstructured data into useful information by auto-tagging images and text-to-speech conversions.

Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.

Developing ML algorithms to analyze huge volumes of historical data to make predictions.

Running tests, performing statistical analysis, and interpreting test results.

Documenting machine learning processes.

Keeping abreast of developments in machine learning.

Machine Learning Engineer Requirements:

Bachelor's degree in computer science, data science, mathematics, or a related field.

Master’s degree in computational linguistics, data analytics, or similar will be advantageous.

At least two years' experience as a machine learning engineer.

Advanced proficiency with Python, Java, and R code writing.

Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.

In-depth knowledge of mathematics, statistics, and algorithms.

Superb analytical and problem-solving abilities.

Great communication and collaboration skills.

Excellent time management and organizational abilities.