• 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.

Role Description The client is seeking a Sr. AI Engineer/Sr. Machine Learning Engineer to design, build, deploy, and maintain production-grade AI systems across their data intelligence platform. This role will lead the development of LLM-powered applications, agentic workflows, retrieval-augmented generation systems, model evaluation pipelines, and scalable AI services. The ideal candidate combines strong machine learning expertise with practical production engineering experience. This person will own complex technical work from concept through deployment, mentor other engineers, and help define best practices for building reliable, observable, and secure AI systems. Essential Responsibilities - Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities. - Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines. - Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources. - Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods. - Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost. - Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review. - Translate prototypes and research concepts into reliable product features that can scale in production. - Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs. - Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices. - Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation. Qualifications - 5+ years of experience in machine learning engineering, AI engineering, data science engineering, or a related technical role. - 2+ years of experience building or shipping production GenAI, LLM, or AI-powered systems. - Advanced Python programming skills and experience building maintainable production software. - Hands-on experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, or similar ML frameworks. - Experience with LLM applications, RAG systems, embeddings, vector databases, prompt engineering, and model evaluation. - Experience deploying AI / ML services using Docker, Kubernetes, CI/CD workflows, APIs, and cloud-native infrastructure. - Strong understanding of classical machine learning, deep learning, NLP, information retrieval, and model validation. - Ability to communicate complex AI concepts clearly to technical and non-technical stakeholders. - Experience mentoring engineers, reviewing technical designs, or leading complex AI engineering initiatives. Preferred Qualifications - Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field. - Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools. - Experience with model-serving platforms such as vLLM, BentoML, Triton, Ray Serve, or similar systems. - Familiarity with ML observability, experiment tracking, model monitoring, and prompt/version management tools. - Experience with graph-based retrieval, knowledge graphs, multimodal models, large-scale data processing, or security-focused data products. - Experience with infrastructure-as-code, workflow orchestration, model routing, caching, batching, or quantization. Special Skills or Experience Required - Proven experience building and deploying production GenAI systems, including LLM applications, agentic workflows, and RAG pipelines. - Advanced Python and ML framework experience, including PyTorch, TensorFlow, Hugging Face Transformers, or similar tools. - Experience with LLM fine-tuning, prompt engineering, embeddings, vector databases, semantic search, and model evaluation. - Strong production engineering skills, including Docker, Kubernetes, CI/CD, model serving, observability, latency optimization, and technical leadership. Success Measures Success in this role will be measured by the delivery of reliable AI capabilities, improved model quality, reduced latency and cost, stronger evaluation coverage, improved observability, and the successful mentorship of other engineers. The role should help increase the speed and confidence with which the company can move AI features from prototype to production.