Kairos Technologies - AI/ML Engineer

Job Description :Role & Responsibilities :Foundational AI/ML & Software Engineering :- Strong grounding in ML fundamentals and software engineering, enabling translation of business problems into robust, scalable AI/ML solutions.- Experience in designing, building and integrating production-grade systems using modern engineering practices (APIs, microservices, CI/CD, containerization).- Ability to bridge classical ML approaches with emerging GenAI paradigms, applying the right techniques to deliver reliable and maintainable solutions.- Effectively leverage AI-assisted development tools (e.g., GitHub Copilot, Claude Code) to accelerate prototyping, improve engineering quality and enhance developer productivity.Product Collaboration & Enablement :- Ability to work effectively within agile product teams, collaborating in iterative cycles to refine requirements, validate hypotheses and deliver incremental AI/ML value.- Ability to drive alignment independently across product, AI/ML engineering, platform and MLOps teams to achieve shared engineering outcomes.- Strong capability in early-stage AI/ML solution development, including problem framing, feasibility assessment, rapid prototyping and iterative experimentation.- Effective collaboration across geographically distributed teams (Denmark, India, Portugal), with strong cross-cultural awareness and communication.Competencies :- 5+ years of experience in software engineering, data or analytics, with strong hands-on experience in AI/ML solution development, including 2- 4+ years focused on AI/ML.- Familiarity with deploying and integrating ML/CV solutions into production environments across cloud and edge systems.ML, Deep Learning & Computer Vision Solution Development :- Hands-on capability in developing ML, deep learning and computer vision solutions for structured data, image/video data and industrial use cases.- Working knowledge of computer vision techniques such as object detection, image classification, segmentation and video analysis, along with deep learning architectures (CNNs, vision transformers, transfer learning and model optimization).- Working knowledge of techniques such as feature engineering, model selection, hyperparameter tuning, transfer learning and model optimization (e.g., pruning, quantization).- Ability to implement end-to-end ML workflows, including data preprocessing, model development, evaluation and deployment, with support for human-in-the-loop and decision-support systems. (ref:hirist.tech) .