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Machine Learning Engineer
Machine Learning Engineer
Zycus's logo

Machine Learning Engineer

madhavi JR's profile picture
Posted by madhavi JR
2 - 13 yrs
₹5L - ₹15L / yr
Bengaluru (Bangalore)
Skills
skill iconMachine Learning (ML)
Natural Language Processing (NLP)
Image Processing
Artificial Intelligence (AI)
skill iconDeep Learning

We are looking for applicants with a strong background in Analytics and Data mining (Web, Social and Big data), Machine Learning and Pattern Recognition, Natural Language Processing and Computational Linguistics, Statistical Modelling and Inferencing, Information Retrieval, Large Scale Distributed Systems and Cloud Computing, Econometrics and Quantitative Marketing, Applied Game Theory and Mechanism Design, Operations Research and Optimization, Human Computer Interaction and Information Visualization. Applicants with a background in other quantitative areas are also encouraged to apply.

We are looking for someone who can create and implement AI solutions. If you have built a product like IBM WATSON in the past and not just used WATSON to build applications, this could be the perfect role for you.

All successful candidates are expected to dive deep into problem areas of Zycus’ interest and invent technology solutions to not only advance the current products, but also to generate new product options that can strategically advantage the organization.

Skills:

  • Experience in predictive modelling and predictive software development
  • Skilled in Java, C++, Perl/Python (or similar scripting language)
  • Experience in using R, Matlab, or any other statistical software
  • Experience in mentoring junior team members, and guiding them on machine learning and data modelling applications
  • Strong communication and data presentation skills
  • Classification (svm, decision tree, random forest, neural network)
  • Regression (linear, polynomial, logistic, etc)
  • Classical Optimization(gradient descent, newton raphson, etc)
  • Graph theory (network analytics)
  • Heuristic optimisation (genetic algorithm, swarm theory)
  • Deep learning (lstm, convolutional nn, recurrent nn)

Must Have:

  • Experience: 3-9 years
  • The ideal candidate must have proven expertise in Artificial Intelligence (including deep learning algorithms), Machine Learning and/or NLP
  • The candidate must also have expertise in programming traditional machine learning algorithms, algorithm design & usage
  • Preferred experience with large data sets & distributed computing in Hadoop ecosystem
  • Fluency with databases

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About Zycus

Founded :
1998
Type :
Product
Size :
1000-5000
Stage :
Profitable

About

Zycus was incorporated in Princeton, the U.S. in 1998. Since then, we have grown every day and established as an organization which now is a leading global provider of complete Source-to-Pay suite of procurement performance solutions. We develop cloud-based (SaaS) Source-to-Pay solutions for large global enterprises, and have successfully deployed about 200 solutions to over 1000 Global clients. Our spirit of innovation and our passion to help procurement create greater business impact is reflected among procurement solution deployments that we have undertaken over the years. We are proud to have as our clients, some of the best-of-breed companies across verticals like Manufacturing, Automotive, Banking and Finance, Oil and Gas, Food Processing, Electronics, Telecommunications, Chemicals, Health and Pharma, Education and more. With a strong workforce of 1200+ employees, we are present in India with 3 Development centers located in Bengaluru, Mumbai, and Pune. We also have our offices in 13 countries including the U.S, UK, Netherlands, Dubai, Singapore, France, Germany, Melbourne, Thailand, Sweden and Denmark. Been recognized as a leader for the 4th time in Gartner's Magic Quadrant (2018, 2017, 2015, 2013), for Strategic Sourcing Application Suites, we stand well-fortified among our peers. Zycus debuted as a leader in The Forrester Wave: eProc Q2, 2017 and was also bracketed as a leader in The Forrester Wave: Contract Life Cycle Management Q1, 2019.
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