As a hands-on AI/LLM Engineer, you will be at the heart of designing and building advanced AI systems that power the modern enterprise.
This is a deeply technical, hands-on role, you will spend the majority of your time in the architecture and engineering of real-world AI solutions across classical machine learning, generative AI, and agentic systems, delivering these within active client engagements.
You will translate requirements into concrete architecture decisions: selecting design patterns, evaluating and benchmarking technical frameworks, assembling reusable components, and making deliberate technology choices that balance innovation with enterprise-grade reliability.
You will design and build AI agent architectures including multi-agent orchestration, tool use, skills use, and memory systems and work hands-on with foundation models through fine-tuning, retrieval-augmented generation (RAG), and custom model integration.
A part of your work will also involve engineering the AI context layer that makes these systems intelligent in practice connecting enterprise knowledge bases, structured and unstructured data sources, and domain-specific content so that AI outputs are grounded, accurate, and relevant to the client's business.
You will design and validate systems against enterprise non-functional requirements across security, observability, governance, performance, and scalability.
A core output of this role is the production of tangible engineering and architecture deliverables.
This means writing and owning software components building, integrating, and testing AI system modules as a practitioner alongside producing detailed architecture artifacts including architecture decision records (ADRs), component diagrams, data flow diagrams, and integration specifications that guide and enable broader engineering teams.
You will work with cross-functional delivery teams alongside data engineers, ML engineers, and application developers, and this role is an opportunity to develop deep expertise across the full AI architecture stack, sharpen your engineering instincts on complex, real-world problems, and build a foundation for growing into a lead or principal architect over time.
THE WORK
Independently design, build, and deliver software components across the AI architecture — owning them end to end from design through implementation, integration, and testing as a hands-on practitioner
Design and build AI agent architectures — including individual agents, their prompts, tools, and skills, multi-agent orchestration, and memory systems — making deliberate design pattern and technology choices
Design and implement agent orchestration patterns that handle task handoffs, communication, state management, and error recovery, validating them through hands-on prototyping




Evaluate multiple design options and technical approaches, making deliberate, justified design choices that balance capability, cost efficiency, performance, and enterprise-grade reliability
Design, build, and run evaluation strategies and harnesses that measure agent and system quality on metrics such as accuracy, relevance, and faithfulness, translating findings into design improvements
Architect and implement foundation model integrations — selecting the right models, invocation patterns, and customization approaches (fine-tuning, RAG, custom integration) based on capability, cost, and performance trade-offs
Design and build model adaptation and fine-tuning pipelines, applying working knowledge of transformer-based architectures to inform model selection and optimization
Design and build the AI context layer — including context graph design and ingestion pipelines that parse, chunk, enrich, and index structured and unstructured enterprise content, and the retrieval components that ground AI outputs in the client's knowledge
Build embedding, vector storage, and retrieval (semantic, hybrid, reranking) into end-to-end RAG pipelines, applying integration patterns that connect to enterprise data sources
Design and implement context assembly and memory components that manage prompts, context windows, and conversational state for grounded, accurate outputs
Identify, design, and build reusable components and solution patterns that accelerate delivery and can be templated across engagements
Design for cost efficiency and performance — optimizing model usage, inference patterns, caching, and resource utilization to meet target latency, throughput, and cost objectives
Design, build, and validate systems against enterprise non-functional requirements — implementing guardrails, prompt-injection defenses, PII handling, and access controls for security and Responsible AI
Build governance controls including versioning, audit logging, and lineage tracking, and produce the model documentation that keeps systems auditable
Build observability into systems — logging, tracing, monitoring, alerting, and cost tracking — to ensure AI solutions remain healthy, performant, and scalable in production
Produce detailed architecture artifacts — including architecture decision records (ADRs), architecture blueprints, design documents,



agent orchestration and integration pattern specifications, component and data flow diagrams — that guide and enable broader engineering teams
Continuously learn, evaluate, and apply new design patterns, frameworks, and technologies across the rapid-evolving AI landscape, balancing innovation with enterprise-grade reliability
Collaborate with cross-functional delivery teams — data engineers, ML engineers, and application developers — to translate requirements into concrete architecture decisions that meet stakeholder needs
EDUCATION
Bachelor's Degree or equivalent
Basic (required) Qualification
Minimum of 3 years of experience in designing & deploying AI / ML solutions using at least one cloud vendor as an AI/ML architect.
Minimum of 1 year of experience in the Agentic, LLM and Generative AI space.
Minimum of 1 year of experience architecting and operationalizing LLM driven application architecture patterns.
Minimum of 3 years in coding and engineering, machine learning, deep learning and NLP solutions and applications.
Minimum of 2 years of coding experience using python
Benefits of working at Accenture
18 weeks paid parental leave
Long & short-term career break opportunities
Structured career development program
Local and international career opportunities.
Certified as a Family Inclusive WorkplaceTM
Flexible Work Arrangements - centered around Accenture's Truly Human ethos and our commitment to supporting the health and wellbeing of our people.
Accenture is a an EEO and Affimate Action Employee of Females/Minorities/Veterans/Individuals with Disabilities.
Equal Employment Opportunity Statementfor Australia:
At Accenture, we recognise that our people are multi-dimensional, and we create a work environment where all people feel like they can bring their authentic selves to work, every day.
Our unwavering commitment to inclusion and diversity unleashes innovation and creates a culture where everyone feels they have equal opportunity.
Our range of progressive policies support flexibility in 'where', 'when' and 'how' our people work to ensure that Accenture is an organisation where you can strive for more, achieve great things and maintain the balance and wellbeing you need.
We encourage applications from all people, and we are committed to removing barriers to the recruitment process and employee lifecycle.
All employment decisions shall be made without regard to age, disability status, ethnicity, gender, gender identity or expression, religion or sexual orientation and we do not tolerate discrimination.
If you require adjustments to the recruitment process or have a preferred communication method, please email ****** and cite the relevant Job Number, or contact us on ***************.
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📌 Ai Llm Senior Engineer (New South Wales)
🏢 Accenture
📍 New South Wales