Key Responsibilities:

• Work with business stakeholders to identify and prioritize use cases for computer vision (e.g., inspection, safety monitoring, asset tracking, counting, anomaly detection).

• Gather and document detailed requirements: environments, device types (fixed CCTV, drones, mobile/handheld), operating constraints, accuracy expectations, and integration needs.

• Analyse current workflows and manual inspection/monitoring processes to identify gaps that can be addressed with computer vision.

• Design solution frameworks and high-level architectures for computer vision use cases, including data capture, model inference, edge vs cloud processing, and integration with existing systems.

• Collaborate with data science and engineering teams to translate business requirements into model requirements (classes, labels, accuracy thresholds, latency constraints).

• Define data collection strategies, annotation guidelines, and quality benchmarks for training datasets.

• Support POCs and pilots: define success criteria, monitor performance metrics, gather user feedback, and recommend iterations.

• Prepare detailed documentation: BRDs, functional specifications, process flows, UI/UX requirements for monitoring dashboards or mobile apps.

• Conduct stakeholder workshops and training sessions to drive adoption of computer vision-based solutions.

Required Skills & Experience

• 4+ years of experience in business analysis, product consulting, or solutions roles involving AI/computer vision projects.

• Conceptual understanding of computer vision techniques (object detection, classification, tracking, segmentation) and their typical limitations.

• Exposure to projects involving cameras, drones, or handheld/mobile devices in real-world environments (industrial, infrastructure, retail, security, etc.).

• Strong ability to convert unstructured business problems into structured use cases and solution requirements.

• Familiarity with data pipelines, edge devices, and integration patterns (APIs, event streams, dashboards).

• Strong analytical skills, with comfort working with metrics such as precision/recall, false positives/negatives, and latency.

• Excellent communication skills, with the ability to work with technical and non-technical stakeholders.

• Strong documentation skills and experience creating BRDs, FRDs, and process diagrams.

Nice-to-Have

• Experience working with or around computer vision platforms, drone software, or video analytics solutions.

• Prior experience in domains like infrastructure inspection, manufacturing, logistics, or public safety.

• Technical background (Engineering/CS/Data) is an advantage, though not mandatory if exposure to AI projects is strong.