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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)
  • Video preprocessing and retrieval (temporal context, clip-level indexing) 
  • Resourceful and meticulous professionals who are passionate about delivering AI-powered capabilities