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Senior ML Engineer

Senior ML Engineer

ConfidentialBengaluru / Bangalore, India
6 days ago
Job description

About TensorIoT

  • AWS Advanced Consulting Partner (for ML and GenAI solutions)
  • Pioneers in IoT and Generative AI products.
  • Committed to diversity and inclusion in our teams.

TensorIoT, an AWS Advanced Tier Services Partner, builds innovative solutions that help organizations unlock the full potential and efficiency of the AWS cloud ecosystem. We specialize in solving complex business challenges with cutting-edge technology, from conceptualizing proofs of concept and minimum viable products to developing production-ready applications. Our team is committed to engineering success for our clients, leveraging the AWS platform to deliver bespoke solutions that drive customer success.

TensorIoT's founders helped build world-class IoT and AI platforms at AWS and Google, and are now creating solutions to simplify the way enterprises incorporate edge devices and their data into their day-to-day operations. Our mission is to help connect devices and make them intelligent. Our founders firmly believe in the transformative potential of smarter devices to enhance our quality of life, and we're just getting started!

TensorIoT is proud to be an equal opportunity employer. This means that we are committed to diversity and inclusion, and encourage people from all backgrounds to apply. We do not tolerate discrimination or harassment of any kind, and make our hiring decisions based solely on qualifications, merit, and business needs at the time.

Job Description

At TensorIoT India team, we look forward to bringing on board senior Machine Learning Engineers / Data Scientists. In this section, we briefly describe the work role, the minimum and the preferred requirements to qualify for the first round of the selection process.

What are the kinds of tasks Data Scientists do at TensorIoT

As a Data Scientist, the kinds of tasks revolve around the data that we have and the business objectives of the client. The tasks generally involve : Studying, understanding, and analyzing datasets; feature engineering, proposing and solutions, evaluating the solution scientifically, and communicating with the client. Implementing ETL pipelines with database / data lake tools. Conduct and present scientific research / experiments within the team and to the client.

Minimum Requirements

  • Masters + 6 years of work experience in Machine Learning Engineering
  • OR
  • B.Tech (Computer Science or related) + 8 years of work experience in Machine Learning Engineering
  • 3 years of Cloud Experience.
  • Experience working with Generative AI (LLM), Prompt Engineering, and tuning of LLMs.
  • Hands-on experience in MLOps (model deployment, maintenance)
  • Hands-on experience with Docker.
  • Clear concepts of the following :
  • Supervised Learning, Unsupervised Learning, Reinforcement Learning
  • Statistical Modelling, Deep Learning
  • Interpretable Machine Learning
  • Well-rounded exposure to Computer Vision, Natural Language Processing, and Time-Series Analysis.
  • Scientific & Analytical mindset, proactive learning, adaptability to changes.
  • Strong interpersonal and language skills in English, to communicate within the team and with the clients.

    • Preferred Qualifications

  • PhD in the domain of Data Science / Machine Learning
  • M.Sc | M.Tech in the domain of Computer Science / Machine Learning
  • Some experience in creating cloud-native technologies, and microservices design.
  • Published scientific papers in the relevant domain of work.

    • CV Tips

    Your CV is an integral part of your application process. We would appreciate if the CV prioritizes the following :

  • Focus :
  • More focus on technical skills relevant to the job description.
  • Less or no focus on your roles and responsibilities as a manager, team lead, etc.
  • Less or no focus on the design aspect of the document.
  • Regarding the projects you completed in your previous companies,
  • Mention the problem statement very briefly.
  • Your role and responsibilities in that project.
  • Technologies & tools used in the project.
  • Always good to mention (if relevant) :
  • Scientific papers published, Master Thesis, Bachelor Thesis.
  • Github link, relevant blog articles.
  • Link to LinkedIn profile.
  • Mention skills that are relevant to the job description and you could demonstrate during the interview / tasks in the selection process.

    • We appreciate your interest in the company and look forward to your application.

    Skills Required

    Machine Learning, Natural Language Processing, Unsupervised Learning, Data Lake, reinforcement learning, Statistical Modelling, Deep Learning, supervised learning, MLops, Docker, Computer Vision, Database

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