Role Description As a Founding Applied AI Engineer at Valent, you will help design and build the core intelligence layer of the product. This role is focused on applying modern AI techniques to messy enterprise problems involving documents, retrieval, reasoning, workflow automation, and human-in-the-loop review. - Help build systems that 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. - Hands-on experience building with LLMs, retrieval systems, document AI, agents, or applied machine learning systems. - Ability to take AI systems from prototype to production. - 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. - Ability to reason from first principles about when AI should automate, assist, or stay out of the way. - Interest in solving messy operational problems, not just building polished 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 foundation of a company. Requirements - Experience with LLM application development. - Familiarity with RAG systems and vector databases. - Experience in document parsing, OCR, extraction, or classification. - Knowledge of evaluation frameworks for AI systems. - Experience in workflow automation or agentic systems. - Background in enterprise SaaS. - Familiarity with compliance, supply chain, manufacturing, regulatory, or quality workflows. - Experience with 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.

• Build AI Agents with domain context for specialized tasks, including multi-step reasoning, tool use, and autonomous decision-making • Design and implement agentic workflows — orchestrating LLMs, retrieval systems, and external tools into reliable, production-grade pipelines • Integrate agents with client applications, APIs, and enterprise systems • Evaluate, iterate on, and optimize agent performance using structured testing, tracing, and observability • Deploy and maintain generative AI solutions in production environments (APIs, microservices, cloud architecture) • Document your work, follow best practices for LLMOps, and help shape AI development standards at Appsilon • Work closely with Data Engineers, Developers, and Analysts to integrate GenAI solutions into full-scale applications

Role Description We’re seeking a Senior AI Engineer to join our Professional Services team to design, build, and operationalise AI solutions that deliver measurable value for enterprise clients, working across the AI lifecycle from prototyping to production. Must be based anywhere in Australia, a citizen, and ideally hold security clearance. - Solution Design & Delivery: Work with clients to understand requirements, shape technical solutions, and deliver Agentic AI and GenAI workloads on Azure. Build production‑grade applications with CI/CD, monitoring, and observability. - GenAI & RAG Engineering: Develop robust RAG pipelines, optimise prompts, improve latency/cost, and design evaluation/test frameworks for LLM‑based systems. - MLOps & Platform Engineering: Implement MLOps with Azure ML/Databricks including experiment tracking, model registry, feature stores, automated deployment, and IaC. Establish operational telemetry and model monitoring (drift, bias, quality). - Data & Integration: Work with data engineers across ADLS, Delta Lake, EventHub, Synapse/Fabric, APIs, and vector databases to enable scalable hybrid retrieval architectures. - Security, Governance & Consulting: Apply enterprise‑grade security (Entra, networking, secrets, RBAC), embed responsible AI practices, and produce HLD/LLDs. Lead workshops, present to stakeholders, mentor engineers, and contribute to reusable components and accelerators. Qualifications - 7–10+ years in software/data/AI engineering, including 1 year in Agentic AI. - Experience delivering enterprise Search/RAG/virtual agent solutions on Azure. - Strong hands‑on skills with Azure, Databricks, and Azure ML. - Proven technical leadership, mentoring, and cross‑team collaboration. - Preferably: code/design artefacts, Azure/Databricks certifications, or related academic background. Requirements - Tools & Platforms: Python, Microsoft Foundry, LangChain/Semantic Kernel, Azure AI Search/Redis/pgvector, ADLS, Delta Lake, Databricks, Synapse/Fabric, GitHub Actions/Azure DevOps, Docker, AKS, Key Vault, Bicep/Terraform, Azure Monitor, Prometheus/Grafana. Benefits - Be part of a team that leverages modern technologies to solve real problems and provides top level of customer satisfaction. - Work with a Microsoft Partner of the Year award winner with multiple specialisations. - Be supported by experienced peers and leaders that value technical expertise and encourage innovation, with clear career pathways and ongoing learning. - Enjoy a supportive workplace that promotes inclusion, flexibility, diversity, and celebrates differences. - Take advantage of largely working from home in our remote/hybrid workplace and enjoy the flexibility to balance your life. - Thrive in a community with strong values: #BeTrue #TeamUp #StandOut #ThinkAhead #FearLessAchieveMore.

Role Description Our client is seeking a Jr. AI Engineer/Jr. Machine Learning Engineer to support the development, testing, and improvement of AI-powered features across their data intelligence platform. This role is designed for an early-career engineer who has strong technical fundamentals, curiosity about GenAI systems, and an interest in learning how production AI products are built and maintained. The Jr. AI Engineer will work closely with senior engineers to assist with: - Prompt experimentation - Data preparation - RAG pipeline support - Model evaluation - Documentation - Debugging - Basic AI service development This role offers hands-on exposure to: - LLMs - Embeddings - Retrieval systems - ML workflows - Production engineering practices Qualifications - 0–2 years of experience in AI engineering, machine learning, software engineering, data science, or a related technical area. - Internship experience, academic work, bootcamp projects, portfolio projects, or open-source contributions are acceptable. - Solid Python programming skills. - Foundational understanding of machine learning, deep learning, NLP, data processing, and model evaluation concepts. - Familiarity with tools or libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, pandas, NumPy, or similar technologies. - Interest in LLMs, GenAI systems, prompt engineering, embeddings, semantic search, RAG, and AI agents. - Ability to work with structured and unstructured data. - Comfort using Git, notebooks, command-line tools, APIs, and collaborative development workflows. - Strong attention to detail, curiosity, problem-solving ability, and willingness to learn from feedback. - Clear written communication skills for documenting technical work and experiment results. Requirements - Portfolio, academic, internship, or project experience involving LLMs, chatbots, semantic search, classification, summarization, automation, or ML workflows. - Exposure to vector databases, embeddings, document processing, information retrieval, or search systems. - Familiarity with Docker, cloud environments, CI/CD concepts, or basic deployment workflows. - Exposure to agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools. - Coursework or practical experience in machine learning, NLP, statistics, data engineering, computer science, or software engineering. - Interest in security analytics, investigations, data intelligence, fraud detection, or enterprise AI systems. Special Skills or Experience Required - Foundational knowledge of machine learning, deep learning, NLP, LLMs, prompt engineering, and RAG concepts. - Solid Python skills with exposure to ML libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, or similar tools. - Experience through coursework, internships, projects, or portfolio work involving AI, data preparation, model testing, search, or automation. - Ability to document experiments, compare model outputs, support debugging, and learn production ML practices such as Git, APIs, Docker, and CI/CD. Success Measures Success in this role will be measured by: - Consistent contribution to AI experiments - Clean and reliable implementation work - Clear documentation - Improved evaluation support - Effective debugging assistance - Steady growth in production AI engineering skills The role should help increase team capacity while developing strong internal AI engineering talent over time.