AI Engineer

• Prompt Engineering & Pipeline Management: Design, iterate, and maintain prompt systems that reliably generate high-quality clinical documentation across a growing number of templates.
• Build scalable prompt architectures that let us add new output formats without fragmentation.
• Evaluate & Integrate AI Models: Benchmark and select the best LLM solutions for clinical documentation. Stay on top of the rapidly evolving model landscape and assess new options.
• Build Evaluation Pipelines: Design practical evaluation processes across our data pipeline from audio and transcription to LLM-generated output. The goal isn't perfection but knowing when output is good enough.
• Ensure Clinical Output Quality: Work closely with our psychologists and product team to define what "good" looks like, ensuring features meet the real-world expectations of users.
• Experiment, Ship, Iterate: Design experiments and data labeling strategies, push them to production, measure real-world performance, and use the insights to drive the next iteration.

Senior AI Engineer, Healthcare Startup

• Collaborate with business stakeholders to identify, prioritize, and scope high-impact AI use cases, including buy-vs-build assessments
• Design and deliver AI products end-to-end, from problem definition through prototype, pilot, and production handover
• Build AI-powered solutions including data pipelines, RAG and retrieval systems, and reasoning workflows, hardened for enterprise production environments
• Deploy, serve, and operate open-source models, and fine-tune them for domain-specific use cases where they outperform or complement vendor offerings
• Integrate with vendor AI platforms and internal AI solutions, reusing existing capabilities where possible
• Instrument solutions to track adoption, quality, business value, ROI, cost, and risk
• Partner with technology teams to ensure solutions are secure, compliant, auditable, and maintainable
• Run rapid evaluation cycles and contribute reusable components, patterns, and tools for wider adoption

• Design and implement real-world agentic AI systems using modern agent frameworks and orchestration tools.
• Develop agentic workflows that go beyond chat, including complex analytical pipelines, multi-step research workflows, tool-using agents, knowledge-grounded agents, and structured decision-support systems.
• Work with knowledge-based AI architectures, including retrieval-augmented generation, knowledge graphs, symbolic rules, structured domain models, ontologies, and hybrid reasoning systems.
• Develop and apply mechanisms for controlling inference, including planning constraints, reasoning policies, guardrails, validation layers, tool-use control, and human-in-the-loop checkpoints.
• Explore and implement neuro-symbolic approaches for agentic reasoning, combining LLM-based reasoning with symbolic, rule-based, graph-based, or formally structured methods.
• Build transparent AI methods that make agent behaviour traceable, explainable, testable, and auditable.
• Create evaluation and testing frameworks for agentic systems, including benchmark tasks, regression tests, failure-mode analysis, trace inspection, robustness testing, and task-level performance measurement.
• Develop full-stack prototypes and production applications, integrating backend services, APIs, databases, frontend interfaces, model providers, and orchestration layers.
• Collaborate with researchers, engineers, product teams, and domain experts to translate ambiguous real-world problems into reliable agentic workflows.
• Stay current with developments in agentic AI, reasoning systems, LLM orchestration, AI evaluation, and applied neuro-symbolic methods.

Lead AI Architect

• Work closely with Product, Engineering, and Security teams to contribute to the development and maintenance of Agentic AI solutions that power our MDR platform, ION.
• Support the building and upkeep of intelligent systems that enhance threat detection, automation, and decision-making.
• Grow from maintaining and contributing to production AI systems toward taking on greater design ownership over time.
• Connect with Product Managers, Engineers, and Security Analysts to understand Ontinue's vision for AI-driven MDR.
• Familiarize yourself with ION, Ontinue's Microsoft-based MDR platform, and in particular the Agentic AI Platform Ontinue develops and maintains.
• Contribute to production bug resolution, debugging, and incremental feature development across existing Agentic AI workflows and data pipelines.
• Begin to understand Ontinue's value proposition and identify opportunities where AI capabilities can make a real difference.

Entry Level AI Engineer

• Build and ship AI features end-to-end (model → system → user experience)
• Design and iterate on prompts, tools, memory, and agent workflows
• Turn raw model outputs into structured, reliable, and predictable behaviors
• Debug issues across the full stack (model, orchestration, infra, UX)
• Optimize for latency, cost, and production reliability
• Develop lightweight evaluation frameworks to measure real-world performance
• Work closely with product and engineering to translate ambiguous problems into working systems

Applied AI Engineer

Computer Vision Engineer

• Contribute to the development of advanced image processing solutions for orthopedic surgery
• Focus on analyzing information from arthroscopic video streams
• Apply image processing and deep learning techniques for real-time analysis
• Implement and optimize algorithms (e.g., SLAM) for surgical environments
• Collaborate with an interdisciplinary team working on surgical planning and navigation software

Arthroscopic Image Processing Engineer

• Own documentation as a system: the pipelines that build and publish it, the AI-augmented workflows that generate drafts for human review and refinement, the review and publish process, and the infrastructure.
• Lead the growth of existing docs-as-code foundation and AI-assisted documentation workflows into a docs-as-AI-agents capability.
• Own documentation site and its publishing as software: docs-as-code repo, CI/CD publishing pipelines, build performance, and automated checks.
• Build AI-augmented documentation workflows: drafting, summarization, classification, consistency and staleness checks, and feedback loops that improve generation quality over time.
• Generate reference documentation from source (OpenAPI and related specs) and keep docs in sync with platform changes.
• Lower the barrier for internal contributors to ship their own docs through the docs-as-code workflow.
• Own release-notes and customer communications cadence with every platform release.
• Maintain documentation style guide and ensure the function is not dependent on any one person.

Senior / Staff Documentation Engineer, AI & Docs Tooling

AI Research Scientist

• Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle.
• Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
• Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks.
• Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques.
• Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success.
• Build and scale training workflows across distributed GPU infrastructure.
• Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks.
• Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development.
• Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements.
• Publish research findings in top-tier AI conferences and journals where applicable.

AI Research Engineer – Multi-Modal, Vision

Machine Learning Engineer

• Design, train, and optimize deep learning models for computer vision tasks in highly dynamic and safety-critical environments
• Own the full ML lifecycle, from data strategy and model architecture to deployment and performance monitoring
• Develop robust evaluation pipelines to ensure models meet strict reliability and safety requirements
• Work on ML certification activities, validating model behavior under defined regulatory frameworks
• Leverage transfer learning and simulation to scale training and testing beyond real-world data limitations
• Collaborate closely with software, systems, and flight teams to integrate ML models into real-world applications
• Continuously push model performance across edge cases, environmental variability, and operational constraints

NLP Engineer

• Drive Research: Lead experiments to answer cutting-edge research questions, designing evaluations and analyzing results
• Innovate: Explore novel approaches and emerging technologies that advance TR's product portfolio
• Collaborate & Disseminate: Work with experienced research scientists and share your findings through internal presentations and external publications

Research Intern – Machine Learning, Natural Language Processing

• **What You´ll Do**
• - Develop and implement computer vision algorithms and deep learning models for localization and mapping, object detection and tracking, classification, general image processing, and related functions - as well as analyze, extend, and improve the existing code base and models.
• - Collaborate with other engineering teams (such as flight testing, hardware, sensor fusion) to integrate computer vision solutions into prototypes and products.
• - Test and evaluate the performance, robustness, and reliability of computer vision systems in diverse conditions, to ensure accuracy, reliability, and efficiency. This may involve both simulated and real-world environments.

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