13+ Clustering Jobs in India

Data Science:

• Python expert level, Analytical, Different models works, Basic concepts, CPG(Domain).

• Statistical Models & Hypothesis , Testing

• Machine Learning Important

• Business Understanding, visualization in Python.

• Classification, clustering and regression

•

Mandatory Skills

• Data Science, Python, Machine Learning, Statistical Models, Classification, clustering and regression

·     IMMEDIATE JOINER

Professional Experience with 5+ years in Confluent Kafka Admin

·    Demonstrated experience design / development.

·    Must have proven knowledge and practical application of – Confluent Kafka (Producers/ Consumers / Kafka Connectors / Kafka Stream/ksqlDB/Schema Registry)

·    Experience in performance optimization of consumers, producers.

·    Good experience debugging issues related offset, consumer lag, partitions.

·    Experience with Administrative tasks on Confluent Kafka.

·    Kafka admin experience including but not limited to setup new Kafka cluster, create topics, grant permissions, offset reset, purge data, setup connectors, setup replicator task, troubleshooting issues, Monitor Kafka cluster health and performance, backup and recovery.

·    Experience in implementing security measures for Kafka clusters, including access controls and encryption, to protect sensitive data.

·    Install/Upgrade Kafka cluster techniques.

·    Good experience with writing unit tests using Junit and Mockito

·    Have experience with working in client facing project.

·    Exposure to any cloud environment like AZURE is added advantage.

·    Experience in developing or working on REST Microservices

Experience in Java, Springboot is a plus

• 3+ years of Experience majoring in applying AI/ML/ NLP / deep learning / data-driven statistical analysis & modelling solutions.
• Programming skills in Python, knowledge in Statistics.
• Hands-on experience developing supervised and unsupervised machine learning algorithms (regression, decision trees/random forest, neural networks, feature selection/reduction, clustering, parameter tuning, etc.). Familiarity with reinforcement learning is highly desirable.
• Experience in the financial domain and familiarity with financial models are highly desirable.
• Experience in image processing and computer vision.
• Experience working with building data pipelines.
• Good understanding of Data preparation, Model planning, Model training, Model validation, Model deployment and performance tuning.
• Should have hands on experience with some of these methods: Regression, Decision Trees,CART, Random Forest, Boosting, Evolutionary Programming, Neural Networks, Support Vector Machines, Ensemble Methods, Association Rules, Principal Component Analysis, Clustering, ArtificiAl Intelligence
• Should have experience in using larger data sets using Postgres Database. 

Intuitive is the fastest growing top-tier Cloud Solutions and Services company supporting Global Enterprise Customer across Americas, Europe and Middle East.

Intuitive is looking for highly talented hands-on Cloud Infrastructure Architects to help accelerate our growing Professional Services consulting Cloud & DevOps practice. This is an excellent opportunity to join Intuitive’s global world class technology teams, working with some of the best and brightest engineers while also developing your skills and furthering your career working with some of the largest customers.

Job Description :

• Extensive exp. with K8s (EKS/GKE) and k8s eco-system tooling e,g., Prometheus, ArgoCD, Grafana, Istio etc.
• Extensive AWS/GCP Core Infrastructure skills
• Infrastructure/ IAC Automation, Integration - Terraform
• Kubernetes resources engineering and management
• Experience with DevOps tools, CICD pipelines and release management
• Good at creating documentation(runbooks, design documents, implementation plans )

Linux Experience :

1. Namespace
2. Virtualization
3. Containers

Networking Experience

1. Virtual networking
2. Overlay networks
3. Vxlans, GRE

Kubernetes Experience :

Should have experience in bringing up the Kubernetes cluster manually without using kubeadm tool.

Observability                              

Experience in observability is a plus

Cloud automation :

Familiarity with cloud platforms exclusively AWS, DevOps tools like Jenkins, terraform etc.

Company Profile:

The company is World's No1 Global management consulting firm.

Job Responsibilities:-

• Develop robust, scalable and maintainable machine learning models to answer business problems against large data sets.
• Build methods for document clustering, topic modeling, text classification, named entity recognition, sentiment analysis, and POS tagging.
• Perform elements of data cleaning, feature selection and feature engineering and organize experiments in conjunction with best practices.
• Benchmark, apply, and test algorithms against success metrics. Interpret the results in terms of relating those metrics to the business process.
• Work with development teams to ensure models can be implemented as part of a delivered solution replicable across many clients.
• Knowledge of Machine Learning, NLP, Document Classification, Topic Modeling and Information Extraction with a proven track record of applying them to real problems.
• Experience working with big data systems and big data concepts.
• Ability to provide clear and concise communication both with other technical teams and non-technical domain specialists.
• Strong team player; ability to provide both a strong individual contribution but also work as a team and contribute to wider goals is a must in this dynamic environment.
• Experience with noisy and/or unstructured textual data.

knowledge graph and NLP including summarization, topic modelling etc

• Strong coding ability with statistical analysis tools in Python or R, and general software development skills (source code management, debugging, testing, deployment, etc.)
• Working knowledge of various text mining algorithms and their use-cases such as keyword extraction, PLSA, LDA, HMM, CRF, deep learning & recurrent ANN, word2vec/doc2vec, Bayesian modeling.
• Strong understanding of text pre-processing and normalization techniques, such as tokenization,
• POS tagging and parsing and how they work at a low level.
• Excellent problem solving skills.
• Strong verbal and written communication skills
• Masters or higher in data mining or machine learning; or equivalent practical analytics / modelling experience
• Practical experience in using NLP related techniques and algorithms
• Experience in open source coding and communities desirable.

Able to containerize Models and associated modules and work in a Microservices environment

DevOps 

Engineers : Min 3 to 5 Years 
Tech Leads : Min 6 to 10 Years 

• Implementing & supporting CI/CD/CT pipelines at scale.  
• Knowledge and experience using Chef, Puppet or Ansible automation to deploy and be able to manage Linux systems in production and CI environments.  
• Extensive experience with Shell scripts (bash).  
• Knowledge and practical experience of Jenkins for CI.  
• Experienced in build & release management.  
• Experience of deploying JVM based applications.  
• Enterprise AWS deployment with sound knowledge on AWS & AWS security.  
• Knowledge of encryption technologies: IPSec, SSL, SSH. 
• Minimum of 2 years of experience as a Linux Systems Engineer (CentOS/Red Hat) ideally supporting a highly-available, 24x7 production environments. 
• DNS providing and maintenance.  
• Helpful skills: Knowledge of applications relying on Maven, Ant, Gradle, Spring Boot.  
• Knowledge of app and server monitoring tools such as ELK/AppEngine. 
• Excellent written, oral communication and interpersonal skills. 

Job Description

We are looking for an experienced engineer to join our data science team, who will help us design, develop, and deploy machine learning models in production. You will develop robust models, prepare their deployment into production in a controlled manner, while providing appropriate means to monitor their performance and stability after deployment.

What You’ll Do will include (But not limited to):

• Preparing datasets needed to train and validate our machine learning models
• Anticipate and build solutions for problems that interrupt availability, performance, and stability in our systems, services, and products at scale.
• Defining and implementing metrics to evaluate the performance of the models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
• Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
• Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
• Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
• Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
• Supporting solutions ranging from rule-bases, classical ML techniques  to the latest deep learning systems.
• Partnering with cross-functional team members to bring large scale data engineering solutions to production
• Communicating your approach and results to a wider audience through presentations

Your Qualifications:

• Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
• Good knowledge of traditional machine learning methods and neural networks
• Experience with practical machine learning modeling, especially on time-series forecasting, analysis, and causal inference.
• Experience with data mining algorithms and statistical modeling techniques for anomaly detection in time series such as clustering, classification, ARIMA, and decision trees is preferred.
• Ability to implement data import, cleansing and transformation functions at scale
• Fluency in Docker, Kubernetes
• Working knowledge of relational and dimensional data models with appropriate visualization techniques such as PCA.
• Solid English skills to effectively communicate with other team members

Due to the nature of the role, it would be nice if you have also:

• Experience with large datasets and distributed computing, especially with the Google Cloud Platform
• Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
• Experience with No–SQL and Graph databases
• Experience working in a Colab, Jupyter, or Python notebook environment
• Some experience with monitoring, analysis, and alerting tools like New Relic, Prometheus, and the ELK stack
• Knowledge of Java, Scala or Go-Lang programming languages
• Familiarity with KubeFlow
• Experience with transformers, for example the Hugging Face libraries
• Experience with OpenCV

About Egnyte

In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com

#LI-Remote

Graphene is an award-winning AI company, developing customized insights and data solutions for corporate clients. With a focus on healthcare, consumer goods and financial services, our proprietary AI platform is disrupting market research with an approach that allows us to get into the mind of customers to a degree unprecedented in traditional market research.

Graphene was founded by corporate leaders from Microsoft and P&G and works closely with the Singapore Government & universities in creating cutting edge technology. We are gaining traction with many Fortune 500 companies globally.

Graphene has a 6-year track record of delivering financially sustainable growth and is one of the few start-ups which are self-funded, yet profitable and debt free.

We already have a strong bench strength of leaders in place. Now, we are looking to groom more talents for our expansion into the US. Join us and take both our growths to the next level!

• Primary Purpose: As part of a highly productive and creative AI (NLP) analytics team, optimize algorithms/models for performance and scalability, engineer & implement machine learning algorithms into services and pipelines to be consumed at web-scale
• Daily Grind: Interface with data scientists, project managers, and the engineering team to achieve sprint goals on the product roadmap, and ensure healthy models, endpoints, CI/CD,
• Career Progression: Senior ML Engineer, ML Architect

• Work in a product-development team capable of independently authoring software products.
• Guide junior programmers, set up the architecture, and follow modular development approaches.
• Design and develop code which is well documented.
• Optimize of the application for maximum speed and scalability
• Adhere to the best Information security and Devops practices.
• Research and develop new approaches to problems.
• Design and implement schemas and databases with respect to the AI application
• Cross-pollinated with other teams.

HARD AND SOFT SKILLS

Must Have

• Problem-solving abilities
• Extremely strong programming background – data structures and algorithm
• Advanced Machine Learning: TensorFlow, Keras
• Python, spaCy, NLTK, Word2Vec, Graph databases, Knowledge-graph, BERT (derived models), Hyperparameter tuning
• Experience with OOPs and design patterns
• Exposure to RDBMS/NoSQL
• Test Driven Development Methodology

Good to Have

• Working in cloud-native environments (preferably Azure)
• Microservices
• Enterprise Design Patterns
• Microservices Architecture
• Distributed Systems