Senior Research Engineer I, Hub of the Future, IAIC

Job Summary

The role focuses on translating AI research prototypes into a robust, integrated and deployment-ready system for operationally representative evaluation. The successful candidate will work closely with research scientists, the solution architect, quality engineers and project stakeholders to integrate the ASR, NLU, LLM and multi-agent components into an end-to-end platform. The role requires strong programming and full-stack engineering capability, together with the discipline to deliver reliable software under performance, security and integration constraints.

Role emphasis: strong software engineering, full-stack development, integration, deployment automation, system hardening and dependable delivery - not model research alone.

Key Responsibilities

1. End-to-End System Integration and Deployment
• Package and deploy AI inference services and supporting application components on project compute and prototype deployment infrastructure.
• Implement and maintain interfaces between the Audio Extraction Gateway, AI inference/model-orchestration platform and services, and user-facing applications.
• Support integration with standard web endpoints, audio codecs, metadata flows and industry-grade interfaces used by the project architecture.
• Build deployment configurations that can be reproduced across development, test, simulated and controlled operational environments.

2. Full-Stack Application Development
• Develop and maintain backend services, APIs, data flows and application logic supporting real-time processing and system operations.
• Develop or support operator-facing web interfaces and dashboards.
• Implement service-to-service communication, asynchronous processing and state management required by the integrated platform.
• Build administrative, configuration and diagnostic functions needed for engineering testing and deployment support.
• Collaborate with UI/UX and end-user stakeholders to translate operational requirements into usable and maintainable software features.
• Write clean, modular and testable code with clear documentation and configuration management.

3. AI/ML Serving and Performance Engineering

• Profile and optimize end-to-end latency, throughput, memory usage, GPU utilization and service concurrency against project performance targets.
• Implement robust timeout, retry, fallback, health-check and degraded-mode behaviours so that failures are controlled and observable.
• Conduct load, stress and endurance testing on target hardware and help identify system bottlenecks across application, network and model-serving layers.

4. Platform, DevOps and Operational Readiness
• Develop containerized deployment workflows and automated build, test and release processes for project software.
• Maintain environment configuration, dependency management, versioning and reproducible deployment artefacts.
• Implement logging, metrics, tracing and diagnostic tooling to support testing, troubleshooting and demonstration activities.
• Support secure deployment within restricted or controlled environments, including access control, configuration hygiene and handling of project data according to applicable requirements.
• Support infrastructure setup for inference servers, prototype deployment servers, storage and endpoint access required for trials and demonstrations.
• Contribute to deployment, infrastructure, maintenance and troubleshooting documentation for subsequent transition and knowledge transfer.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, Computer Engineering, Information Systems or a related technical discipline.
• Strong programming skills and demonstrated ability to build reliable software systems.
• Strong full-stack development experience covering backend services/APIs and modern web application development.
• Solid understanding of software architecture, data structures, concurrency, networking fundamentals and distributed/service-oriented systems.
• Hands-on experience with Linux-based development and deployment environments.
• Experience with Git, automated testing and disciplined software development practices.
• Demonstrated ability to debug complex end-to-end systems and resolve issues across multiple software layers.
• Strong delivery mindset: able to take technical work from requirements and prototype through integration, testing and demonstrable working software.
• Good written and verbal communication skills and ability to work effectively with research, engineering and domain-expert teams.

Strongly Preferred Experience

• Backend development using Python frameworks or equivalent service technologies, and design of RESTful or similar service APIs.
• Frontend development using a modern JavaScript/TypeScript framework such as React, Vue or Angular.
• Docker/container-based deployment, CI/CD pipelines and environment automation.
• GPU-enabled AI/ML inference deployment, model serving or integration of LLM/ASR/NLP components into production-like applications.
• Real-time, streaming or event-driven systems and performance-sensitive application design.
• Observability tooling for application logs, metrics, tracing, monitoring and incident diagnosis.
• Deployment to on-premise servers, secure enterprise environments or restricted networks.
• Database and data-store integration, including structured operational data and application state.
• Load testing, performance profiling, capacity testing and optimization of distributed applications.
• Security-conscious engineering practices, including secrets handling, access control, dependency management and auditability.

What We Are Looking For

• A builder who is comfortable moving between code, APIs, UI, infrastructure and deployment issues to get an end-to-end system working.
• Strong ownership and accountability for delivery, with attention to correctness, maintainability and operational reliability.
• Ability to work effectively in an R&D environment where components evolve, while still imposing sound software engineering discipline.
• Pragmatic problem-solving: able to isolate bottlenecks, make sensible engineering trade-offs and close integration gaps quickly.
• Interest in deploying modern AI systems in demanding real-world environments rather than working only on standalone application features.