Job
Description
The ideal candidate for the position of AL/ML Architect should possess a B.SC, B.E/B.Tech./M.E./M.S./M.Tech degree with a strong academic record. With at least 10 years of overall experience, including 4-5 years in an Architect role, the candidate should also have 4+ years of hands-on experience applying statistical/ML algorithms and techniques to real-world data sets. Additionally, 4+ years of experience as a developer working on scalable and secure applications is required, along with 2+ years of experience independently designing core product modules or complex components. Proficiency in GenAI is a must-have for this role. Key Responsibilities: The responsibilities of this role include designing and overseeing the architecture of ML systems, encompassing data pipelines, model selection, deployment strategies, and ensuring scalability and performance. The candidate should have a strong understanding of AI algorithms, data engineering, and software architecture. Collaboration with cross-functional teams to translate business needs into effective ML solutions is essential. Defining the architecture of ML systems, from data ingestion to deployment pipelines, and selecting appropriate ML algorithms based on data characteristics and business objectives are crucial aspects of the role. Designing and implementing data pipelines for data collection, cleaning, transformation, and feature engineering is also a key responsibility. Ensuring that ML systems can handle large volumes of data and deliver efficient predictions is vital. The candidate should have in-depth knowledge of multiple technological and architectural areas, applicable processes, methodologies, standards, products, and frameworks. Defining and documenting architecture, capturing non-functional requirements, preparing estimates, and defining technical solutions to proposals are part of the role. Providing technical leadership, mentoring a team of developers, and collaborating with multiple teams to make technical decisions are essential aspects. Practices such as reuse, defect prevention, process optimization, automation, and productivity enhancement should be adopted. Skills: The candidate should have expert knowledge of languages such as Python, proficiency in Probability, Statistics, and Linear Algebra, and proficiency in Machine Learning, Deep Learning, NLP (GenAI is an added advantage). Familiarity with data science platforms, tools, and frameworks is required. Designing scalable processes for collecting, manipulating, presenting, and analyzing large datasets in a production-ready environment is essential. Experience with large volumes of data, algorithms, and prototyping is necessary. Knowledge of Nifi, Kakfa, HDFS, Big Data, MongoDB, Cassandra, and CI/CD pipelines with tools like GIT, Jenkins, Maven, Ansible, Docker is preferred. Expertise or appreciation in Security products such as End-point detection, protection and response, Managed detection and response is also desirable for this role.,
