Job Opportunities API

Securitas Group Securitas is a world-leading safety and security solutions partner that helps make your world a safer place. By leveraging technology in partnership with our clients, we offer a broad portfolio of value-enhancing services and solutions integrated across the security value chain – from on-site services to advanced monitoring, comprehensive risk prediction and advisory services. With around 322 000 employees in 44 markets, our innovative, holistic approach with local and global expertise makes us a trusted business partner to many of the world’s best-known companies. Benefitting from almost nine decades of deep experience and guided by our values of integrity, vigilance, and helpfulness, we create sustainable value by helping our clients optimize their operations and protect what matters most - their people and assets. AI Team at Securitas At Securitas, our colleagues show up every day to help keep communities and organizations safe. Our job in the AI team is to make sure they're equipped with the best tools and intelligence possible . We are Securitas' Specialized AI Team - the internal center of excellence for advanced, custom AI. We don't work on generic tools or off-the-shelf solutions. We build the AI capabilities that require deep technical and domain expertise, and that directly move the needle on how Securitas operates at scale. About the role You will be a key technical voice in a small, focused team - someone who shapes how we approach problems, not just solves them. You'll lead the design, development, and production deployment of ML and GenAI solutions that turn raw data into actionable intelligence across a range of real-world problems. The stack we work with Python · PyTorch · LLMs (OpenAI, Gemini, Claude, open-source) · RAG pipelines, Hugging Face · Python analytical tools (DuckDB, polars, Pandas, and more) · Streamlit & Dash · Claude Code · GitHub Copilot · React · Databricks · Docker · SQL/NoSQL · Azure/GCP Responsibilities Owning the architecture of LLM-powered pipelines that extract structure and insight from large volumes of unstructured text, such as incident reports, operational logs, client data. Designing and stress-testing end-to-end GenAI architectures (e.g. RAG), prompt strategies, and evaluation frameworks - relevance, faithfulness, hallucination rates, latency tradeoffs - and setting the bar for what "good" looks like on the team. Building and productionizing workforce management models - demand forecasting, shift scheduling optimization, and attrition modeling - that help deploy officers more effectively. Developing client churn models that give the business early, actionable retention signals. Driving the end-to-end ML lifecycle: from problem framing and data strategy through to monitored, production-grade systems. Translating ambiguous business problems into concrete technical roadmaps - and pushing back when the framing is wrong. Mentoring junior data scientists and setting technical standards across the team. Presenting findings, model behavior, and tradeoffs to senior stakeholders clearly and credibly. What you'll bring Must-haves Around 5+ years of professional data science experience, with a clear track record of successes. Deep Python skills and strong software engineering habits - your code is readable, tested, and maintainable. Advanced NLP experience and hands-on work with LLMs at a level beyond prompt experimentation - fine-tuning, evaluation, deployment. A rigorous approach to GenAI evaluation : you've built frameworks to measure output quality, catch failure modes, and make principled tradeoffs with full lifecycle thinking. Experience with MLOps fundamentals : deployment, serving, and monitoring of models, CI/CD, Docker, application and service logging, and reproducible pipelines. Using modern AI coding tools to work as a highly product