for ML Research Engineer

RESPONSIBILITIES

- Delivering an end-to-end solution, starting with requirements comprehension, developing research proposals, implementing the research by experimenting with various methodologies and algorithms, documenting, and presenting outcomes.
- Utilize programming, data modelling, analytical and statistical techniques to analyse vast amounts of data and produce insights that can be put to use.
- Create and test hypotheses, then plan experiments to address specific, challenging questions.
- Project documentation includes corporate goals, data collection and processing procedures,

leading strategies, the final algorithm, and a comprehensive list of outcomes and analytical metrics.

- Establish best practises and procedures, share information with the team, mentor more junior team members, and contribute to the shared code library.
- Take ownership of your tasks and have end to end responsibility from planning stage to presenting results

QUALIFICATIONS

- At least 5+ years of working experience in development with at least 4+ years of relevant experience in developing AI/ML based solutions
- At least 4+ years of hands-on data science experience using Python for data analytics,

visualization, statistical programming and data mining for problem-solving. Domain knowledge and experience in Internet of Things (IoT) is a plus.





- Demonstrated expertise in managing end-to-end research POCs, including the design, execution,

analysis, and conclusions of your research, techniques, and outcomes.

- Excellent understanding of data science, machine learning, deep learning (CV, NLP) techniques,

algorithms, and toolkits to develop and deploy scalable solutions for production.

- Applied mathematical and statistical skills, such as statistical analysis, distributions, multivariate testing, regression, classification, and optimization algorithms, etc. to create, test, analyse, and explain results.
- Well familiar with Edge computing and neural networks using Tensorflow Lite, TinyML.
- Familiar with MLOps tools like MLflow or Kubeflow, deep learning frameworks like TensorFlow,

or Pytorch.

- Working experience with big data systems (Hadoop, Spark, Hive) is an advantage.

OTHER SKILLS

- Excellent communication abilities, including the capacity to translate complicated technical concepts and convey their significance to a variety of audiences.
- Having the ability to turn corporate objectives into analysis that can be used.
- A track record of working with teams who are spread out geographically
- Should be a self-starter with a high level of initiative.

📌 AI/ML Lead (Bengaluru)
🏢 Quess Corp / Havells
📍 Bengaluru