Responsibilities:
ï‚· Understanding business objectives and developing models that help to achieve them, along
with metrics to track their progress
ï‚· Implementing data science prototypes by applying appropriate ML algorithms and tools
ï‚· Running hypothesis tests, performing statistical analysis, and interpreting test results
ï‚· Solving complex problems with multi-layered data sets, as well as optimizing existing
machine learning libraries and frameworks
ï‚· Analysing the ML algorithms that could be used to solve a given problem and ranking them
by their success probability
ï‚· Documenting machine learning processes and keeping abreast of developments in machine
learning & ML-Ops
ï‚· Assessing, implementing & deploying cloud infrastructure, data pipelines to meet deadlines
and the business objectives while minimizing costs
ï‚· Deploying ML pipelines to production using CI/CD, and scaling ML algorithms to analyse
huge volumes of historical data to make predictions in a batch or streaming environment
while meeting the required SLAs
ï‚· Consulting with client partners to assess their current ML-Ops maturity, answer any
questions they may have, and design an ML-Ops strategy (roadmap, tech stack & associated
nuances) to meet their requirements
Skills & Requirements:
 Bachelor's/ Master’s degree in computer science, data science, mathematics, or a related
field
ï‚· At least 2-3 years' experience as a machine learning engineer
ï‚· Experience in deploying models on the cloud, and awareness of tools like Sagemaker, ML-
Flow and other ML-Ops tools
ï‚· Advanced proficiency with Python & PySpark
ï‚· Extensive knowledge of ML frameworks, libraries, data structures, data modelling, and
software architecture. Knowledge of deep learning framework such as TensorFlow/PyTorch
is a plus
ï‚· In-depth knowledge of mathematics, statistics, and algorithms
ï‚· Superb analytical and problem-solving abilities
ï‚· Great communication and collaboration skills
ï‚· Excel