Lead II

Role Description
Role Proficiency:

Design and develop ML solutions that will enable intelligent experiences and provide value. Formulate AI scope by working collaboratively with business technology and product teams to understand the product objectives with some guidance from Specialist 1

Outcomes

• Identify and formulate business problem to AI / ML related problems; identifying and communicating AI Scope with stake holders
• Executes relevant data wrangling activities related to the problem
• Conduct ML experiments to understand the feasibility; building baseline models to solve the business problem
• Fine tune the model for optimum performance
• Test Models internally per acceptance criteria from business
• Identify areas and techniques to optimize the model based on test results
• 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 of goals to team members
• Identify metrics for validating the models with the ability to communicate the same in business terms to the product teams.
• Keep track of trends and do rapid prototyping to understand the feasibility of using it in existing solutions
• Visualise and build more complex models / solutions which address scalable solutions. Work with product teams in planning and execution of new product releases
• Mentor junior data scientists in their delivery solutions
• Work with product design and product management teams identifying design interventions of ML Models with guidance from Specialist ML Engineer I

Measures Of Outcomes

• Selection of right algorithms for business problems
• Successful deployment of the model with optimised accuracy for baseline model
• 100 % Adherence to project schedule / timelines
• Personal and team achievement of 100% of quarterly/yearly objectives (OKR Assignments HIG Stretch goals)
• Publish internal testing observations and refine the model to achieve 100% business objectives
• Independently or with help of product team / ML Specialist identify business metrics and corresponding model metrics.
• Identify areas to improve the model using new technologies for improvement of product / feature
• Scalability of the ML solutions for complex problems

Outputs Expected
Design to deliver Product Objectives:

• Design ML solutions aligned to achievement of product objectives
• Understand the business requirements and formulate into an ML problem
• Define data requirements for the model building and model monitoring; working with product managers to get necessary data
• Define the data requirements for the problem
• Define the AI scope and metrics from the product and business objectives with guidance from Lead II
• Check the validity of the training data and test data requirements from a performance standpoint and take necessary actions

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

• Perform necessary research to use the latest state of the art techniques to design scalable approaches
• Explain the relevance of technologies its pros and cons to the product team to enable appropriate design experiences

Technology Innovation

• Convey innovation in the way of problem solving. Publish work in leader boards or as patents with guidance from the Specialist ML Engineer I

Skill Examples

• Technically strong with the ability to connect the dots
• Ability to communicate the relevance of technology to the stakeholders in a simple and relatable language
• Capable of selecting appropriate techniques based on the data availability; setting expectations on the overall functionality of the solutions
• Understand the limitation of the current technology; defining the AI scope and metrics
• Curiosity to learn more about new business domains and Technology Innovation
• An empathetic listener who can give and receive honest thoughtful feedback
• Ability to work with product design and product management team to suggest User control features for monitoring the ML models with guidance from Specialist ML Engineer I

Knowledge Examples

• Expertise in machine learning model building lifecycle

• Clear understanding of various ML techniques and its 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
• Aware of the techniques of validating the quality of the data
• Experience in identifying the testing criteria to validate the quality of the model output
• Expertise in 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
Responsibilities Lead the development of fruit defect detection models using object detection and image segmentation techniques. Design and implement deep learning pipelines using frameworks like PyTorch or TensorFlow. Work closely with domain experts to define defect categories and edge cases (e.g., bruises, rot, discoloration, deformities). Build, manage, and optimize data pipelines—including dataset curation, labeling workflows, and augmentation strategies. Ensure high model performance in terms of accuracy, recall, and inference speed—across diverse lighting and background conditions. Collaborate with product and engineering teams to deploy models to production (cloud or edge-based inference). Research and apply cutting-edge computer vision techniques (e.g., YOLOv8, EfficientDet, Mask R-CNN, ViTs, or DETR). Lead and mentor junior ML engineers and researchers. Own model evaluation and explainability tools for business and QA teams. Requirements 10+ years of experience in AI/ML with a focus on computer vision and deep learning. Strong expertise in object detection and image classification techniques. Proven experience working with real-world noisy image datasets and model optimization. Proficient in Python and frameworks such as PyTorch or TensorFlow. Familiar with tools such as OpenCV, Label Studio, Roboflow, or CVAT. Solid understanding of CNNs, transfer learning, and data-centric AI practices. Experience deploying models in production environments (REST APIs, ONNX, TensorRT, etc.).

Skills
Python,Machine Learning,Artificial Intelligence