Establish production-readiness standards and ensure new utilities have appropriate monitoring, alerting, ownership, documentation, rollback strategies, and support plans before launch
Explore and implement Artificial Intelligence-assisted capabilities, where appropriate, to analyse documents and evidence, identify potential control gaps and improve the efficiency and consistency of second-line checks, with appropriate human oversight.
ML services and LLM APIs: Deploy models behind APIs and in processing pipelines for assisted labeling and automated data checks. Own their reliability in production, and integrate LLM APIs into internal tools to help users investigate failures and resolve operational issues.
Cross-Team Collaboration: Act as a technical focal point between AI researchers, software engineers, and business stakeholders to turn complex requirements into workable prototypes.
Build retrieval-based solutions that allow AI applications to efficiently work with large codebases and engineering documentation, using techniques such as embeddings, vector search and hybrid retrieval
Cloud Infrastructure & DevOps: Ensure the resilience and scalability of our mission-critical applications by managing CI/CD pipelines, containerization (Docker/Kubernetes), and cloud-native services to guarantee seamless uptime under pressure.
Conducting thorough and constructive code reviews — ensuring code quality, consistency, readability, and adherence to team standards across every pull request.
The next leap in AI isn't about getting smarter — it's about becoming yours. Seventy years of AI research keep proving the same thing: general methods win over clever designs. Models are getting stronger. Protocols are converging. Infrastructure is commoditizing. But one layer remains scarce — the layer that makes AI truly yours.
Put cutting-edge technologies, algorithms, and models into production by designing and implementing DevOps/DevSecOps pipelines, test environments, and simulators for software verification and validation.
Clarify requirements where required and translate business or user needs into technical specifications, implementation tasks, and acceptance considerations.
Contribute to motion and attitude control development, including PID parameter tuning, control logic implementation, state machine design, and enhancement of system protection and safety mechanisms.
Participate in embedded software architecture design, module splitting and coding standard formulation, develop reusable, stable and low-power embedded software codes adapted to hardware chip features.