• Take complete ownership and deliver major AI engineering features within agreed timelines. • Own AI output quality, structure, and predictability across all user-facing AI interactions. • Design, implement, and maintain output-type-based AI systems, including segmentation, routing, and enforcement. • Ensure consistent output structure and formatting across different LLMs for the same request type. • Integrate and orchestrate multiple LLM providers via OpenRouter, managing model selection, fallback strategies, and cost optimisations. • Design and orchestrate tool-using and agentic AI workflows, defining clean tool contracts (including MCP-based tools), function-calling interfaces, and reliable AI-to-system integrations. • Build and maintain complex, multi-step LLM workflows, including with orchestration frameworks such as LangChain or LlamaIndex, for advanced reasoning, context reuse, and retrieval. • Design and manage production prompt systems with dynamic prompting, context injection, and conditional logic. • Own the deployment and release of LLM experiments, prompt management, and Langfuse-based evaluation pipelines. • Run A/B tests across models, analyse results, and present data-driven impact assessments of AI features and experiments. • Monitor AI system metrics, quality signals, latency, and release health using Langfuse and other observability tools. • Deep-debug complex LLM chains using Langfuse traces, identifying bottlenecks and optimising for cost, latency, and context-window usage, and build output-scoring systems to root-cause hallucinations and logic errors. • Write clean, scalable, and maintainable TypeScript code across the Next.js and Node.js stack. • Build reliable backend logic for AI systems, with strong error handling, request validation, fallback flows, and predictable behaviour in production, including reliable tool execution and AI-to-service integrations. • Ensure high code quality through testing, code reviews, and clear engineering standards. • Monitor, troubleshoot, and improve production performance, reliability, and system health. • Drive maintainability and technical quality through solid architecture, refactoring, and disciplined release practices.

• Implement AI-powered product features and workflows based on specifications from senior engineers. • Write, iterate on, and manage production prompt templates following established patterns for dynamic prompting and context injection. • Participate in prompt experimentation workflows by drafting variants, running A/B tests across models via OpenRouter, and documenting results. • Integrate structured output schemas (JSON mode, function calling, Zod/JSON schemas) to ensure AI responses are predictable and application-ready. • Build and maintain output enforcement mechanisms such as validators and repair loops under senior guidance. • Help implement tool calling and function calling integrations so AI features can fetch data or trigger actions, following patterns established by senior engineers. • Contribute to evaluation pipelines and help assess prompt and model quality using Langfuse. • Write clean, maintainable TypeScript code within the Next.js and Node.js stack. • Participate in code reviews and incorporate feedback. • Document technical decisions and system behaviour to support knowledge sharing. • Collaborate with product, growth, data, and billing teams to deliver features on time.

• Implement AI-powered product features and workflows based on specifications from senior engineers. • Write, iterate on, and manage production prompt templates following established patterns for dynamic prompting and context injection. • Participate in prompt experimentation workflows by drafting variants, running A/B tests across models via OpenRouter, and documenting results. • Integrate structured output schemas (JSON mode, function calling, Zod/JSON schemas) to ensure AI responses are predictable and application-ready. • Build and maintain output enforcement mechanisms such as validators and repair loops under senior guidance. • Help implement tool calling and function calling integrations so AI features can fetch data or trigger actions, following patterns established by senior engineers. • Contribute to evaluation pipelines and help assess prompt and model quality using Langfuse. • Write clean, maintainable TypeScript code within the Next.js and Node.js stack. • Participate in code reviews and incorporate feedback. • Document technical decisions and system behaviour to support knowledge sharing. • Collaborate with product, growth, data, and billing teams to deliver features on time.

Role Description As a Junior Computer Vision Engineer at InnovationTeam, you will be responsible for designing, developing, and deploying vision-based AI systems for real-world, production environments. You will work closely with cross-functional teams to build scalable image and video analytics solutions. - Develop and train Computer Vision models for image and video analysis. - Implement solutions for object detection, image classification, segmentation, and tracking. - Prepare and manage datasets, including data cleaning, labelling, and augmentation. - Optimize models for GPU performance, inference speed, and scalability. - Deploy AI models into production using APIs and containerized services. - Integrate Computer Vision models with broader AI and analytics platforms. - Collaborate with software engineers, data scientists, and product teams. - Maintain documentation and ensure code quality and reproducibility. Qualifications - Minimum 5 years of hands-on experience in AI / Machine Learning/Deep learning with a focus on Computer Vision. - Master’s degree in computer science, software Engineering, Artificial Intelligence, Engineering, or a related field. - Strong knowledge of Computer Vision concepts and algorithms. - Practical experience with CNN-based models and modern CV architectures. - Proficiency in Python and common ML/CV libraries (PyTorch or TensorFlow, OpenCV). - Experience with object detection and segmentation frameworks (e.g., YOLO, Detectron2). - Understanding of model evaluation, metrics, and performance optimization. - Experience deploying models in cloud or on-prem environments. - Familiarity with Docker and basic MLOps practices. - Excellent English communication skills (written and spoken). Requirements - Experience with video analytics or real-time inference. - Exposure to Vision Transformers or multimodal AI. - Experience with GPU-based training and inference. - Knowledge of cloud platforms (OCI, AWS, Azure, or GCP). - Background in domains such as healthcare, smart cities, or industrial AI. - Work on practical, production-grade Computer Vision solutions. - Access to GPU infrastructure and modern AI tooling. - Collaborative, engineering-focused work environment. - Opportunities for growth and advanced AI exposure. - Competitive compensation package.