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Developer III - Software Engineering - ML
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Role description
Role Proficiency:
Independently develops error free code with high quality validation of applications guides other developers and assists Lead 1 – Software Engineering
Outcomes:
- Understand and provide input to the application/feature/component designs; developing the same in accordance with user stories/requirements.
- Code debug test document and communicate product/component/features at development stages.
- Select appropriate technical options for development such as reusing improving or reconfiguration of existing components.
- Optimise efficiency cost and quality by identifying opportunities for automation/process improvements and agile delivery models
- Mentor Developer 1 – Software Engineering and Developer 2 – Software Engineering to effectively perform in their roles
- Identify the problem patterns and improve the technical design of the application/system
- Proactively identify issues/defects/flaws in module/requirement implementation
- Assists Lead 1 – Software Engineering on Technical design. Review activities and begin demonstrating Lead 1 capabilities in making technical decisions
Measures of Outcomes:
- Adherence to engineering process and standards (coding standards)
- Adherence to schedule / timelines
- Adhere to SLAs where applicable
- Number of defects post delivery
- Number of non-compliance issues
- Reduction of reoccurrence of known defects
- Quick turnaround of production bugs
- Meet the defined productivity standards for project
- Number of reusable components created
- Completion of applicable technical/domain certifications
- Completion of all mandatory training requirements
Outputs Expected:
Code:
- Develop code independently for the above
Configure:
- Implement and monitor configuration process
Test:
- Create and review unit test cases
scenarios and execution
Domain relevance:
- Develop features and components with good understanding of the business problem being addressed for the client
Manage Project:
- Manage module level activities
Manage Defects:
- Perform defect RCA and mitigation
Estimate:
- Estimate time
effort
resource dependence for one's own work and others' work
including modules
Document:
- Create documentation for own work as well as perform peer review of documentation of others' work
Manage knowledge:
- Consume and contribute to project related documents
share point
libraries and client universities
Status Reporting:
- Report status of tasks assigned
- Comply with project related reporting standards/process
Release:
- Execute release process
Design:
- LLD for multiple components
Mentoring:
- Mentor juniors on the team
- Set FAST goals and provide feedback to FAST goals of mentees
Skill Examples:
- Explain and communicate the design / development to the customer
- Perform and evaluate test results against product specifications
- Develop user interfaces business software components and embedded software components 5 Manage and guarantee high levels of cohesion and quality6 Use data models
- Estimate effort and resources required for developing / debugging features / components
- Perform and evaluate test in the customer or target environment
- Team Player
- Good written and verbal communication abilities
- Proactively ask for help and offer help
Knowledge Examples:
- Appropriate software programs / modules
- Technical designing
- Programming languages
- DBMS
- Operating Systems and software platforms
- Integrated development environment (IDE)
- Agile methods
- Knowledge of customer domain and sub domain where problem is solved
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.).
Skills
Python,Machine Learning,Artificial Intelligence
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