ML Architect

Responsibilities:

• Involved 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 to 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 and mentor and guide the 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, and productivity enhancement.

Requirements:

• 10+ years overall with 4-5 years as an 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 of experience independently designing core product modules or complex components.
• Hands-on experience with GenAI.
• Expert knowledge of languages such as Python.
• Proficiency in Probability, Statistics, and Linear Algebra.
• Proficiency in Machine Learning, Deep Learning, NLP, [GenAI is an add-on].
• Familiarity with data science platforms, tools, and frameworks.
• Designs scalable processes to collect, manipulate, present, and analyze large datasets in a production-ready environment.
• Experience with large volumes of data, algorithms, and prototyping.
• NiFi, Kafka, HDFS, Big Data, MongoDB, Cassandra.
• CI/CD pipelines with tools like Git, Jenkins, Maven, Ansible, and Docker.
• Preferable appreciation or expertise in security products such as endpoint detection, protection, and response, managed detection and response, etc.