Are you an ML Engineer with 3+ years of experience in optimizing machine learning systems? Uplers Talent Network is the place for you!
Uplers is inviting ML Engineers with extensive experience of 3+ years to join our Talent Network. With excellent opportunities, we are looking for intelligent and experienced ML Engineers who can research, modify, and apply data science and data analytics prototypes.
Uplers Talent Network seeks professionals to join the Network who can adapt to challenges and work for global companies to gain exposure and opportunities.
If you think you’re the one with a strong desire to become an expert in your industry and work internationally, Uplers Talent Network is the platform for you!
Location : Permanently remote- Experience : 3+ years
- Working Hours : 8 hours per day (5 days/week) Apply Now
Uplift with Uplers
We are proud to say that each day at Uplers is about its people.
Being a People First organization, our culture believes in looking at our team as the foundation of Uplers’ success. We believe that a happy working environment enhances innovation which is why we have strived to create a people-focused culture over the years. We also provide equal opportunities to all because our people are the real heroes of Uplers.
Let’s talk about Uplers Talent Solutions
Uplers Talent Solutions is a catalyst to every candidate looking out for a once-in-a-lifetime opportunity to work for global companies.
Perks of joining Uplers Talent Solutions:
- Payout : Take a long stride and earn 1.5x more than industry standards.
- Talent Success Coach : A dedicated coach will connect you with opportunities and guide you before as well as after hiring.
- Projects : You will be ensured global exposure and learnings with our exciting global projects.
- Flexibility : You can work from the comfort of your living room’s couch or from a breezy beach.
- Opportunity : You can set aside the uncertainty of job security and rely on us for a full-time opportunity.
How to become a part of our Talent Pool?
About the profile
- If you are an ML Engineer with 3+ years of experience and believe you can be in India’s top 3.5% engineers, we invite you to go through the requirements below and take at least 3 skills assessments to get into our Talent Network.
- The more successful assessments you add to your profile, the better would be your chances of being a part of our Talent Network.
Roles & Responsibilities:
- To research, modify, and apply data science and data analytics prototypes.
- To create and construct methods and plans for machine learning
- Employing test findings to do statistical analysis and improve models
- To search the internet for training datasets that are readily available
- ML systems and models should be trained and retrained as necessary
- To improve and broaden current ML frameworks and libraries
- To create machine learning applications in accordance with client or customer needs
- To investigate, test, and put into practice appropriate ML tools and algorithms.
- To evaluate the application cases and problem-solving potential of ML algorithms and rank them according to success likelihood
- To better comprehend data through exploration and visualization, as well as to spot discrepancies in data distribution that might affect a model’s effectiveness when used in practical situations
Primary Skills:
- Data Modelling
- Natural Language Processing - Word2vec, Sentiment Analysis, Summarization, Gensim, and NLTK
- Unix & Linux Servers
- Programming Languages - C, C++, Python and Java
- Spark and Hadoop
Requirements:
- Advanced math and statistics knowledge, particularly in the areas of calculus, linear algebra, and Bayesian statistics
- Advanced degree in math, computer science, statistics or a related field
- A master’s degree in artificial intelligence, deep learning, or a related discipline
- Strong teamwork, problem-solving, and analytical skills.
- Abilities in software engineering
- Knowledge of data science
- Languages for coding and programming, such as Python, Java, C++, C, R, and JavaScript
- Practical understanding of ML frameworks
- Practical familiarity with ML libraries and packages
- Recognize software architecture, data modeling, and data structures
- Understanding of computer architecture