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We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.
Skills:
- 5+ years deploying Machine Learning pipelines in large enterprise production systems.
- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.
Roles and Responsibilities:
Deploying ML models into production, and scaling them to serve millions of customers.
Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.
Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.
Provide software design and programming support to projects.
Qualifications & Experience:
Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.
- Mandatory - Hands on experience in Python and PySpark.
- Build pySpark applications using Spark Dataframes in Python using Jupyter notebook and PyCharm(IDE).
- Worked on optimizing spark jobs that processes huge volumes of data.
- Hands on experience in version control tools like Git.
- Worked on Amazon’s Analytics services like Amazon EMR, Lambda function etc
- Worked on Amazon’s Compute services like Amazon Lambda, Amazon EC2 and Amazon’s Storage service like S3 and few other services like SNS.
- Experience/knowledge of bash/shell scripting will be a plus.
- Experience in working with fixed width, delimited , multi record file formats etc.
- Hands on experience in tools like Jenkins to build, test and deploy the applications
- Awareness of Devops concepts and be able to work in an automated release pipeline environment.
- Excellent debugging skills.
Hi,
We are hiring for Data Scientist for Bangalore.
Req Skills:
- NLP
- ML programming
- Spark
- Model Deployment
- Experience processing unstructured data and building NLP models
- Experience with big data tools pyspark
- Pipeline orchestration using Airflow and model deployment experience is preferred
Data Engineer- Senior
Cubera is a data company revolutionizing big data analytics and Adtech through data share value principles wherein the users entrust their data to us. We refine the art of understanding, processing, extracting, and evaluating the data that is entrusted to us. We are a gateway for brands to increase their lead efficiency as the world moves towards web3.
What are you going to do?
Design & Develop high performance and scalable solutions that meet the needs of our customers.
Closely work with the Product Management, Architects and cross functional teams.
Build and deploy large-scale systems in Java/Python.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Create data tools for analytics and data scientist team members that assist them in building and optimizing their algorithms.
Follow best practices that can be adopted in Bigdata stack.
Use your engineering experience and technical skills to drive the features and mentor the engineers.
What are we looking for ( Competencies) :
Bachelor’s degree in computer science, computer engineering, or related technical discipline.
Overall 5 to 8 years of programming experience in Java, Python including object-oriented design.
Data handling frameworks: Should have a working knowledge of one or more data handling frameworks like- Hive, Spark, Storm, Flink, Beam, Airflow, Nifi etc.
Data Infrastructure: Should have experience in building, deploying and maintaining applications on popular cloud infrastructure like AWS, GCP etc.
Data Store: Must have expertise in one of general-purpose No-SQL data stores like Elasticsearch, MongoDB, Redis, RedShift, etc.
Strong sense of ownership, focus on quality, responsiveness, efficiency, and innovation.
Ability to work with distributed teams in a collaborative and productive manner.
Benefits:
Competitive Salary Packages and benefits.
Collaborative, lively and an upbeat work environment with young professionals.
Job Category: Development
Job Type: Full Time
Job Location: Bangalore
- Play a critical role as a member of the leadership team in shaping and supporting our overall company vision, day-to-day operations, and culture.
- Set the technical vision and build the technical product roadmap from launch to scale; including defining long-term goals and strategies
- Define best practices around coding methodologies, software development, and quality assurance
- Define innovative technical requirements and systems while balancing time, feasibility, cost and customer experience
- Build and support production products
- Ensure our internal processes and services comply with privacy and security regulations
- Establish a high performing, inclusive engineering culture focused on innovation, execution, growth and development
- Set a high bar for our overall engineering practices in support of our mission and goals
- Develop goals, roadmaps and delivery dates to help us scale quickly and sustainably
- Collaborate closely with Product, Business, Marketing and Data Science
- Experience with financial and transactional systems
- Experience engineering for large volumes of data at scale
- Experience with financial audit and compliance is a plus
- Experience building a successful consumer facing web and mobile apps at scale
Company Overview:
Rakuten, Inc. (TSE's first section: 4755) is the largest ecommerce company in Japan, and third largest eCommerce marketplace company worldwide. Rakuten provides a variety of consumer and business-focused services including e-commerce, e-reading, travel, banking, securities, credit card, e-money, portal and media, online marketing and professional sports. The company is expanding globally and currently has operations throughout Asia, Western Europe, and the Americas. Founded in 1997, Rakuten is headquartered in Tokyo, with over 17,000 employees and partner staff worldwide. Rakuten's 2018 revenues were 1101.48 billions yen. -In Japanese, Rakuten stands for ‘optimism.’ -It means we believe in the future. -It’s an understanding that, with the right mind-set, -we can make the future better by what we do today. Today, our 70+ businesses span e-commerce, digital content, communications and FinTech, bringing the joy of discovery to more than 1.2 billion members across the world.
Website : https://www.rakuten.com/">https://www.rakuten.com/
Crunchbase : https://www.crunchbase.com/organization/rakuten">Rakuten has raised a total of https://www.crunchbase.com/search/funding_rounds/field/organizations/funding_total/rakuten">$42.4M in funding over https://www.crunchbase.com/search/funding_rounds/field/organizations/num_funding_rounds/rakuten">2 rounds
Companysize : 10,001 + Employees
Founded : 1997
Headquarters : Tokyo, Japan
Work location : Bangalore (M.G.Road)
Please find below Job Description.
Role Description – Data Engineer for AN group (Location - India)
Key responsibilities include:
We are looking for engineering candidate in our Autonomous Networking Team. The ideal candidate must have following abilities –
- Hands- on experience in big data computation technologies (at least one and potentially several of the following: Spark and Spark Streaming, Hadoop, Storm, Kafka Streaming, Flink, etc)
- Familiar with other related big data technologies, such as big data storage technologies (e.g., Phoenix/HBase, Redshift, Presto/Athena, Hive, Spark SQL, BigTable, BigQuery, Clickhouse, etc), messaging layer (Kafka, Kinesis, etc), Cloud and container- based deployments (Docker, Kubernetes etc), Scala, Akka, SocketIO, ElasticSearch, RabbitMQ, Redis, Couchbase, JAVA, Go lang.
- Partner with product management and delivery teams to align and prioritize current and future new product development initiatives in support of our business objectives
- Work with cross functional engineering teams including QA, Platform Delivery and DevOps
- Evaluate current state solutions to identify areas to improve standards, simplify, and enhance functionality and/or transition to effective solutions to improve supportability and time to market
- Not afraid of refactoring existing system and guiding the team about same.
- Experience with Event driven Architecture, Complex Event Processing
- Extensive experience building and owning large- scale distributed backend systems.
- Responsible for gathering, crunching, collecting raw data.
- Own all data needs in the growth team and across key cross-functional initiatives from data extraction, dashboard creation, and data analysis.
- Provide insights and recommended actions as response to current situation, trend, as well as preventive one for better preparation.
- Collaborate with data engineering and cross-functional stakeholders to define data requirements, create dashboard, to drive business decisions and optimize business outcomes
- Manage any regular reporting and tracking.
- Deliver analysis, insights, reporting, data marts, and tools to support the business team.
- Build & maintain data mart and dashboards for tracking business OKR and initiatives.
- Ingest both internal and external data to support business needs.
Qualification:
- Bachelor's degree in Engineering, Mathematics, Statistics, Operation Research or other related disciplines.
- Having 2 - 5 years experience will be an advantage, but fresh graduates are welcomed to apply as well.
- Expert in Spreadsheet, SQL and strong experience with Data Visualization and Reporting tools (e.g. Tableau, Google Data Studio)
- Comfortable working independently and collaboratively with minimal guidance.
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Solve problems in speech and NLP domain using advanced Deep learning and Machine Learning techniques. Few examples of the problems are -
* Limited resource Speaker Diarization on mono-channel recordings in noisy environment.
* Speech Enhancement to improve accuracy of downstream speech analytics tasks.
* Automated Speech Recognition for accent heavy audio with a noisy background.
* Speech analytic tasks, which include: emotions, empathy, keyword extraction.
* Text analytic tasks, which include: topic modeling, entity and intent extraction, opinion mining, text classification, and sentiment detection on multilingual data.
A typical day at work
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You will work closely with the product team to own a business problem. You will then model the business problem into a Machine Learning problem. Next you will do literature review to identify approaches to solve the problem. Test these approaches, identify the best approach, add your own insights to improve the performance and ship that to production!
What should you know?
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* Solid understanding of Classical Machine Learning and Deep Learning concepts and algorithms.
* Experience with literature review either in academia or industry.
* Proficiency in at least one programming language such as Python, C, C++, Java, etc.
* Proficiency in Machine Learning tools such as TensorFlow, Keras, Caffe, Torch/PyTorch or Theano.
* Advanced degree in Computer Science, Electrical Engineering, Machine Learning, Mathematics, Statistics, Physics, or Computational Linguistics
Why DeepAffects?
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* You’ll learn insanely fast here.
* Esops and competitive compensation.
* Opportunity and encouragement for publishing research at top conferences, paid trips to attend workshop and conferences where you have published.
* Independent work, flexible timings and sense of ownership of your work.
* Mentorship from distinguished researchers and professors.