Job Description

Responsibilities

. AI Solution Development

. Design, develop, train, and deploy AI/ML models for:

. Video Analytics

. Object Detection and Tracking

. Facial Recognition

. License Plate Recognition (LPR/ANPR)

. Person Re-Identification

. Behaviour Analytics

. Multimodal AI applications

. Generative AI and LLM-based solutions

. Build end-to-end AI pipelines from data ingestion through model deployment and monitoring

. Evaluate and benchmark state-of-the-art AI models and frameworks

. Design and build user-facing application layers across the full CV lifecycle: data curation tools, training dashboards, evaluation, deployment workflows, and monitoring

. Own backend services (Python, FastAPI, gRPC) that power automated CV pipelines-integrating data prep, augmentation, model training, and evaluation into seamless agent-driven workflows

. Build scalable APIs with clean contracts, versioning, and documentation that internal teams and platform agents consume

. Integrate and wrap CV toolkits (PyTorch, OpenCV, Ultralytics YOLO) so the platform can orchestrate them programmatically

. Implement orchestration layers for multi-step pipelines with retry, rollback, and SLA guarantees

Requirements

. Degree in Computer Science, Engineering, Information Technology, Data Analytics, Data Science, Image Processing or equivalent competencies

. At least 3 years of experience in production deployment of AI solutions

. Competent in at least one programming language (e.g. Python, JavaScript,Java). Interest and ability to gain expertise in other programming languages, frameworks, and technologies

. Able to contribute to architecting and engineering for AI would be a strong plus

. Proven track record delivering AI projects from concept to deployment

Knowledge and experience with any of the following software engineering domains would be an advantage:

. Web development

. End-to-end implementation of production-grade solutions, optimized for scalability, stability and performance

. Big data technologies

. Container technologies

. Data analytics, machine learning and artificial intelligence

. Design, implement, and evaluate deep learning algorithms and models

. Computer Vision foundational concepts

. Vision-language models

. Image preprocessing and retrieval (semantic and visual retrieval, detection, embeddings etc)