Data Science Architect

Qualifications:

The successful candidate should have either of the below

B.SC, B.E/B.Tech./M.E./M.S./M.Tech with good academic record

Experience:

10+ years overall with 4-5 years as Architect

4+ years of experience applying statistical/ML algorithms and techniques to real-world data sets.

4+ years as a developer with experience in related scalable and secure applications.

2+ years experience independently designing core product modules or complex components

Hands on Experience on GenAI

Key Responsibilities

Involves in Designing and Overseeing the overall architecture of ML systems, including data pipelines, model selection, deployment strategies, and ensuring scalability and performance.

Require a strong understanding of AI algorithms, data engineering, and software architecture, while collaborating with cross-functional teams to translate business needs into effective ML solutions.

Define the overall architecture of ML systems, considering data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment pipelines.

Evaluate and select appropriate ML algorithms based on data characteristics and business objectives.

Design and implement data pipelines for data collection, cleaning, transformation, and feature engineering.

Ensure ML systems can handle large volumes of data and deliver efficient predictions.

You are expected have depth/breadth of knowledge of specified multiple technological areas, and architectural areas, which includes knowledge of applicable processes, methodologies, standards, products and frameworks.

You would be responsible for defining and documenting architecture, capturing and documenting non-functional (architectural) requirements, preparing estimates and defining technical solutions to proposals (RFPs).

You should provide technical leadership, mentor and guide team of developers, who would be developing the solution.

You need to collaborate with multiple teams to arrive at technical and tactical decisions. You should contribute and adopt practices such as reuse, defect prevention, process optimization, process automation, productivity enhancement.

Skills

Expert knowledge of languages such as Python

Proficiency in Probability, Statistics and Linear Algebra

Proficiency in Machine Learning, Deep Learning, NLP, [GenAI is add on]

Familiarity with Data science platforms, Tools and frameworks

Designs scalable processes to collect, manipulate, present, and analyse large datasets in a production-ready environment

Experience with large volumes of data, algorithms, and prototyping

Nifi, Kakfa, HDFS, Big Data, MongoDB, Cassandra.

CI/CD pipelines with tools like GIT, Jenkins, Maven, Ansible,Docker

Prefer great appreciation or expertise in Security products such as End point detection, protection and response, Managed detection and response etc