• Build and extend the eval pipeline: how we measure task completion, catch regressions, and turn production failures into fixed behaviors. • Work on context management: what the agent sees, when, and how we keep long-horizon tasks coherent without burning tokens. • Design sub-agent patterns: when to fan out, how to compose specialized agents cleanly, and how to keep the parent agent in control of the outcome. • Own document parsing: turning supplier-uploaded invoices, catalogs, and contracts into structured context the agent can reason over. • Wrap Coupa supplier APIs as agent-callable CLI tools, with clean error surfaces and sensible defaults for an agent caller. • Ship supplier-facing skills on top of the harness: the procedural instructions and tool compositions that let the agent handle specific tasks end-to-end. • Debug messy production behavior: why did the agent take that path, where did it get confused, and what tool, context, or harness change fixes it? • Partner with the senior engineer on harness architecture calls as you find gaps while working across the stack.

• Design and build the agent harness — skill loading, tool invocation, context management, the execution sandbox, compaction patterns and sub-agents. • Stand up the evaluation pipeline: how we measure skill effectiveness, catch regressions, and turn production failures into fixed behaviors. • Partner with product and engineering to ship the first 3–5 supplier-facing use cases (invoicing, catalog, strategic views) on top of the platform you build. • Define and maintain the cloud deployment and configuration for the platform. How it gets to production, how it scales, and how it stays secure. • Make architectural calls on runtime isolation, credential handling, audit logging, and tenant separation, in collaboration with DevOps specialists. • Raise the bar for the rest of the team on how to build with agentic tooling, what to automate, what to verify, and what to leave to humans.

Role Description As an AI Engineer at Valent, you will help design and build the core intelligence layer of the product. This is a hands-on engineering role for someone who likes turning messy real-world business processes into reliable software. - Extract structured information from supplier documents, specifications, certificates, declarations, questionnaires, audit evidence, and regulatory files. - Power AI-assisted workflows for document collection, evidence packet assembly, customer requests, and audit preparation. - Use retrieval, ranking, and document understanding to help users find the right evidence across fragmented sources. - Generate draft responses, summaries, checklists, and compliance artifacts that remain reviewable and auditable by humans. - Build evaluation pipelines to measure quality, reduce hallucination, and improve reliability over time. - Design AI workflows that know when to act autonomously and when to escalate to a human. - Integrate AI features into a polished enterprise product experience. Qualifications - Strong software engineering fundamentals and experience shipping production systems. - Experience with LLMs, retrieval systems, document AI, agents, or applied machine learning. - Comfortable working across the stack when needed. - Ability to write clean, maintainable code and make sound technical tradeoffs. - Care about reliability, observability, testing, and product quality. - Can reason from first principles about when AI should automate, assist, or stay out of the way. - Interest in messy operational workflows, not just clean demos. - Clear communication in writing and comfort working remotely. - Ability to overlap for part of the day with U.S. Eastern Time. - Desire to join early and help shape the technical foundation of a company. Requirements - Experience with LLM application development. - Familiarity with RAG systems and vector databases. - Knowledge of document parsing, OCR, extraction, or classification. - Experience with evaluation frameworks for AI systems. - Background in workflow automation or agentic systems. - Experience in enterprise SaaS. - Familiarity with compliance, supply chain, manufacturing, regulatory, or quality workflows. - Proficiency in React, TypeScript, Node.js, Python, Postgres, or cloud infrastructure. Benefits - Work remotely from India on a U.S.-based enterprise AI product. - Build AI into workflows where the value is operational, measurable, and immediate. - Join early enough to have meaningful influence over architecture, product, and culture. - Work on hard problems at the intersection of AI, enterprise workflow, compliance, and manufacturing. - Help define what modern compliance operations software should look like. How to apply Send your resume, LinkedIn profile, GitHub, portfolio, or anything else that shows how you think and build. We are especially interested in examples of products, systems, or AI workflows you have shipped. Please follow our LinkedIn company page so we know you are not a bot :)

Role Description We are currently developing ARAIAS — an AR- and AI-based hands-free training and assistance system for chronic wound care. The system enables 3D wound capture, AI-supported analysis, and standardized remote expertise and documentation directly at the point of care. - Design and build AI models for wound analysis — from architecture decisions through evaluation and iteration, with a focus on getting to reliable, clinically meaningful outputs. - Apply computer vision techniques — object detection, segmentation, depth estimation, or 3D reconstruction — to real medical imaging challenges. - Handle pre-processing and post-processing of multi-sensor imaging data — including 2D images, depth maps, and other sensor inputs. - Design and build REST APIs and cloud infrastructure that connect and support all system components. - Prototype hardware-software integration — interfacing with smartglass SDKs and sensor APIs to establish data capture pipelines from device to back-end. - Integrate AI models into application layers — inference endpoints, model serving, versioning, and performance monitoring. - Make pragmatic architectural decisions appropriate for the current prototyping stage. - Set up CI/CD, containerization, and basic observability to keep the team moving fast. - Support integration points between the back-end / AI layer and the Unity-based front-end. Qualifications - Hands-on experience with computer vision — image segmentation, object detection, and classification. - Experience designing and adapting model architectures — going beyond basic fine-tuning to make informed decisions about model structure, loss functions, and training strategies. - Ability to evaluate model outputs critically — not just metrics, but understanding what the results mean in context. - Back-end engineering proficiency — REST API design with Python as the primary language. - Cloud platform experience (AWS / GCP / Azure). - Comfort working with hardware SDKs and APIs, and able to independently navigate technical documentation and integration guides to establish device-to-server data flows. - Comfortable with Docker and basic CI/CD pipelines. - Familiarity with ML tooling: PyTorch or TensorFlow. - Fluent in English, German is a bonus. - Ability to take initiative, own problems end-to-end, and make pragmatic technical decisions independently. Requirements - Database experience — PostgreSQL, NoSQL, object storage (S3 / GCS). - Unity experience or familiarity with Unity's integration patterns. - Familiarity with semi-supervised learning — leveraging limited or partially labeled data, relevant when annotated wound imaging data is scarce. - Experience with 3D data pre-processing and post-processing — depth map handling, point cloud cleaning, mesh reconstruction, or similar. - Familiarity with hardware SDK integration — experience interfacing with smart glasses, depth sensors, or similar hardware to extract and stream sensor data. - Exposure to multi-modal learning — combining RGB and depth or LiDAR data into unified model inputs. Benefits - Meaningful work — You help shape how technology redefines education and training in healthcare, with real impact on clinical outcomes. - Ownership and creative freedom — You work autonomously, contribute your ideas directly, and have a genuine say in how the system is built. - A learning culture — We support personal and professional growth through knowledge sharing, access to innovative tools and methods, and a dedicated learning budget. - Flexible working — Remote-friendly setup with the option to work from our Munich office. - Flat hierarchies and team spirit — A small, open, and trust-based team where communication is direct and everyone's voice matters. - International collaboration — Work alongside a Taiwan-based partner covering technology, hardware, and research.