Role Proficiency:

Design and develop ML solutions that will enable intelligent experiences and provide value. Collaboratively work with business technology and product teams to understand the product objectives and formulate the ML problem under minimal guidance from Lead II

Outcomes:

      Executes relevant data wrangling activities related to the problem       Conduct ML experiments to understand feasibility; building baseline models to solve the business problem       Fine tune the baseline model for optimum performance       Test Models internally per acceptance criteria from the 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 and communicate the same in business terms to the product teams.   Keep track of the trends and do rapid prototyping to understand the feasibility of utilizing in existing solutions

Measures of Outcomes:

      Selection of right algorithms for the business problems       Successful deployment of the model with optimised accuracy for baseline model       Number of time project schedule / timelines adhered to       Personal and team achievement of quarterly/yearly objectives (OKR Assignments HIG Stretch goals)       Number of internal testing observations published and models refined to achieve 100 % business objectives with mentoring from the Lead ML Engineer       Number of business metric and corresponding model metrics identified independently or with assistance from product team / ML Specialist       Number of areas identified for improving the model using new technologies for product / feature improvements Number of Rapid prototypes using state of the art methods

Outputs Expected:

Design to deliver Product Objectives:

Design ML solutions which are aligned to and achieve product objectives Understand the business requirements; 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 requirement for the problem Define the AI scope and metrics from the product and business objectives with guidance from Lead II Identify technology components for Rapid prototype Alignment of Business metrics to Model Metrics Check the validity of the training data and test data requirements from the 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 the technologies
its pros and cons to the product team to enable appropriate 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 and relatable language      Capability in selecting the appropriate techniques based on the data availability and set expectations on the overall functionality of the solutions      Ability to understand the limitations 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

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       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:

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 7+ 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.).