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Responsibilities
Architect, build, and deploy RAG pipelines, including chunking, embeddings, vector stores, retrieval, ranking, grounding, and evaluation.
Design and implement Graph RAG solutions leveraging knowledge graphs for multi‑hop reasoning and structured retrieval.
Build robust, scalable ML/LLM services using Python (and Java where applicable) with well‑designed APIs and microservices.
Develop data processing pipelines for ingestion, transformation, metadata extraction, and indexing.
Implement observability, monitoring, evaluation harnesses, automated testing, and CI/CD for GenAI services.
Optimize retrieval quality, response accuracy, latency, and cost across model + retrieval layers.
Apply responsible AI, security, and governance practices for LLM systems (e.g., content filtering, guardrails, model monitoring).
Collaborate with product, data engineering, and cloud platform teams to translate business problems into robust AI solutions.
Produce clear documentation, design specs, and operational runbooks for all delivered components.
Required Qualifications
3+ years of experience as an ML Engineer, AI Engineer, or similar role.
Hands‑on experience building GenAI applications and RAG systems end‑to‑end.
Strong proficiency in Python for ML/LLM development.
Experience with vector databases (e.g., pgvector, Pinecone, Weaviate, FAISS) and embedding models.
Knowledge of LLM frameworks (LangChain, LlamaIndex, Transformers, etc.).
Strong understanding of cloud environments (AWS/Azure) and containerized deployments.
Solid software engineering foundations — APIs, microservices, version control, testing, CI/CD.
Experience with data pipelines, ETL/ELT, and processing unstructured data.
Ability to evaluate retrieval quality, implement ranking strategies, and build evaluation datasets.
Excellent communication skills and ability to work in cross‑functional teams.
Preferred Qualifications
Graph RAG experience using knowledge graphs, graph databases, or graph‑based retrieval.
Experience with Java for backend services, data processing, or connector development.
Familiarity with MLOps/LLMOps tooling and practices.
Experience integrating AI outputs into metadata/catalog systems or workflows.
Experience with prompt engineering, guardrailing, and LLM safety controls.
What We’re Looking For
A builder who loves engineering elegant, reliable systems that scale — someone who understands both machine learning and strong software engineering practices, is passionate about GenAI, and is excited to push the boundaries of RAG and Graph RAG capabilities.
Special Factors
Sponsorship
Vanguard is not offering visa sponsorship for this position.About Vanguard
At Vanguard, we don't just have a mission—we're on a mission.
To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.
How We Work
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
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