14+ Clustering Jobs in India
Apply to 14+ Clustering Jobs on CutShort.io. Find your next job, effortlessly. Browse Clustering Jobs and apply today!
Machine Learning (ML)Position: ML/AI Engineer (3+ years)
Responsibilities:
Design, develop, and deploy machine learning models with a focus on scalability and performance.
Implement unsupervised algorithms and handle large datasets for inference and analysis.
Collaborate with cross-functional teams to integrate AI/ML solutions into real-world applications.
Work on MLOps frameworks for model lifecycle management (optional but preferred).
Stay updated with the latest advancements in AI/ML technologies.
Requirements:
Proven experience in machine learning model building and deployment.
Hands-on experience with unsupervised learning algorithms (e.g., clustering, anomaly detection).
Proficiency in handling Big Data and related tools/frameworks.
Exposure to MLOps tools like MLflow, Kubeflow, or similar (preferred).
Familiarity with cloud platforms such as AWS, Azure, or GCP.
Location: Baner, Pune
Employment Type: Full-time
Data Science
PythonMachine Learning (ML)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
Docker· 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
Data ScienceMachine Learning (ML)
Deep Learning- 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.
Data ScienceMachine Learning (ML)XressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Our
vision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2C
logistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.
Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trusted
logistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, an
extensive logistics network reach, and a seamless last mile management system.
While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Our
big focus areas for the very near future include strengthening our presence as service providers of choice and
leveraging the power of technology to drive supply chain efficiencies.
Job Overview
XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join
the team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning
and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you
will build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need to
understand business requirements and convert them to a solvable data science problem statement. You will be
involved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in
production.
Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmic
components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and
serve the customer better.
You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to
the organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.
Opportunities for application:
- Route Optimization
- Address / Geo-Coding Engine
- Anomaly detection, Computer Vision (e.g. loading / unloading)
- Fraud Detection (fake delivery attempts)
- Promise Recommendation Engine etc.
- Customer & Tech support solutions, e.g. chat bots.
- Breach detection / prediction
An Artificial Intelligence Engineer would apply himself/herself in the areas of -
- Deep Learning, NLP, Reinforcement Learning
- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..
- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs
- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization
- Computer Vision and OCR technologies
The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs
without needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds and
Enterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to find
opportunities for product and process optimization and using models to test the effectiveness of different courses of
action. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,
building, and implementing models, using/creating algorithms, and creating/running simulations. They must be
comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion
for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.
Roles & Responsibilities
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,
Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that
work out of the box.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,
and others.
● Leverage Cloud technology –AWS, GCP, Azure
● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,
Hadoop, HBase, Spark, etc
● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to
drive business solutions.
● Mine and analyze data from company databases to drive optimization and improvement of product
development, marketing techniques and business strategies.
● Assess the effectiveness and accuracy of new data sources and data gathering techniques.
● Develop custom data models and algorithms to apply to data sets.
● Use predictive modeling to increase and optimize customer experiences, supply chain metric and other
business outcomes.
● Develop company A/B testing framework and test model quality.
● Coordinate with different functional teams to implement models and monitor outcomes.
● Develop processes and tools to monitor and analyze model performance and data accuracy.
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Deliver machine learning and data science projects with data science techniques and associated libraries
such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good
to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep
learning or related approaches as it applies to financial applications.
● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for
successful delivery.
What is required of you?
You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadly
distributed, multi-tenant cloud environment.
● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering
● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.
● Experience with building high-performance, resilient, scalable, and well-engineered systems
● Experience in CI/CD and development best practices, instrumentation, logging systems
● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights
from large data sets.
● Experience working with and creating data architectures.
● Good understanding of various machine learning and natural language processing technologies, such as
classification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.
● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,
Boosting, Trees, text mining, social network analysis, etc.
● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.
● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,
simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,
AdWords, Crimson Hexagon, Facebook Insights, etc.
● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.
● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,
D3, ggplot, Tableau etc.
● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests, and proper usage, etc.) and experience with applications.
● Experience building data pipelines that prep data for Machine learning and complete feedback loops.
● Knowledge of Machine Learning lifecycle and experience working with data scientists
● Experience with Relational databases and NoSQL databases
● Experience with workflow scheduling / orchestration such as Airflow or Oozie
● Working knowledge of current techniques and approaches in machine learning and statistical or
mathematical models
● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.
Kafka)
● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring down
serving latency.
● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)
● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.
Data AnalyticsWe will build a comprehensive backtesting platform for trading in the NSE F&O segment.
Any knowledge of financial markets is a bonus
KubernetesDockerIntuitive 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 :
- Namespace
- Virtualization
- Containers
Networking Experience
- Virtual networking
- Overlay networks
- 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.
Data AnalyticsPython
R ProgrammingCompany Profile:
The company is World's No1 Global management consulting firm.
Job Qualifications
Graduate or post graduate degree in statistics, economics, econometrics, computer science,
engineering, or mathematics
2-5 years of relevant experience
Adept in forecasting, regression analysis and segmentation work
Understanding of modeling techniques, specifically logistic regression, linear regression, cluster
analysis, CHAID, etc.
Statistical programming software experience in R & Python, comfortable working with large data
sets; SAS & SQL are also preferred
Excellent analytical and problem-solving skills, including the ability to disaggregate issues, identify
root causes and recommend solutions
Excellent time management skills
Good written and verbal communication skills; understanding of both written and spoken English
Strong interpersonal skills
Ability to act autonomously, bringing structure and organization to work
Creative and action-oriented mindset
Ability to interact in a fluid, demanding and unstructured environment where priorities evolve
constantly and methodologies are regularly challenged
Ability to work under pressure and deliver on tight deadlines
Data ScienceMachine Learning (ML)R ProgrammingPythonDeep LearningJob 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
JenkinsDocker
Amazon Web Services (AWS)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.
Python2. Can demonstrate an understanding of the
following concepts
● Common Linux Utilities
● Server/Application Architecture and
Security Hardening
● Virtualization
● Load Balancing
● Networking Concepts
3. Install, configure, manage and maintain all Linux
server-based applications
4. Strong Knowledge in redhat Clustering
5. Ensure all Linux based systems have the
appropriate patches installed, includes but is not
limited to, security, applications, and OS
6. Ensure all Linux based systems are running
current versions of antivirus software with
current virus definition files
7. Follow the Policies and procedures at the client
location
Data ScienceMachine Learning (ML)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
Deep LearningPythonMachine Learning (ML)WE ARE GRAPHENE
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!
WHAT WILL THE ENGINEER-ML DO?
- 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
YOU CAN EXPECT TO
- 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
Amazon Web Services (AWS)
Follow Cutshort