Role Proficiency:

Under guidance from Senior ML Engineers develop ML models that provides accurate results with controls to solve the business problem identified using state of art techniques.

Outcomes:

      Executes relevant data wrangling activities related to the problem in order to create dataset       Conduct ML experiments to understand the feasibility and build baseline models to solve the business problem       Fine tune the baseline model for optimum performance       Test Models internally per acceptance criteria from business       Document relevant Artefacts for communicating with the business       Work with data scientists to deploy the models.       Work with product teams in planning and execution of new product releases.       Set OKRs and success steps for self/ team and provide feedback to goals for team members       Work with cross functional teams - business technology and product teams to understand the product vision; building ML solutions that provides value to the product

Measures of Outcomes:

      Selection of the appropriate approach to the problem       Number of successful deployments of the model with optimised accuracy for baseline model       Adherence to project schedule / timelines       Personal and team achievement of quarterly/yearly objectives (OKR Assignments HIG Stretch goals)

Outputs Expected:

Design to deliver Product Objectives:

Design ML solutions which are aligned to and achieve product objectives Define data requirements for the model building and model monitoring; working with product managers to get necessary data

Updated on state of art techniques in the area of AI / ML :

Perform necessary research using the latest and state of art techniques to design scalable approaches Explain the relevance of the technologies
its pros and cons to the product team; enabling accurate design experiences

Skill Examples:

     Technically strong with the ability to connect the dots      Ability to communicate the relevance of technology to the stakeholders in a simple relatable language      Curiosity to learn more about new business domains and Technology Innovation      An empathetic listener who can give and receive honest thoughtful feedback

Knowledge Examples:

      Expertise in machine learning model building lifecycle       Clear understanding of various ML techniques with appropriate use to business problems       A strong background of statistics and Mathematics       Expertise in one of the domains – Computer Vision Language Understanding or structured data       Experience in executing collaboratively with engineering design user research teams and business stakeholders       Experience with data wrangling techniques preprocessing and post processing requirements for ML solutions       Good knowledge python and deep learning frameworks like Tensorflow Pytorch Caffe       Familiar with the machine learning model testing approaches       A genuine eagerness to work and learn from a diverse and talented team

Additional Comments:

• B.Tech/MTech or MS.in a Computer Science/Statistics/Data Science or related field • 4+ years of building products with Machine Learning, Deep Learning (DNNs, CNNs, RNNs/LSTMs) • Understanding of and interest in the full machine learning lifecycle, including deploying trained machine learning models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch • Understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques • Experience in Software Engineering including programming in Python • Production experience implementing machine learning pipelines and models at scale in Python, Java or similar languages • Design, prototype, and validate ML/NLP models to solve challenging cutting-edge problems, using state-of-the-art techniques (including LLM) and best practices • Experience with model optimization • Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) is a plus