Senior Data Scientist / Generative AI Engineer - Agentic AI
Summary
Lead the design and delivery of an agentic digital assistant inside Microsoft Teams for a major financial services client, building LLM-driven features, RAG pipelines, and persona-driven reporting using Python, AWS, and GenAI/LLM technologies.
The opportunity We're partnering with a leading AI consultancy to place a hands-on Senior Data Scientist / Generative AI Engineer into a high-profile engagement with a major financial services organisation.
This is a genuine builder's role. You'll lead the design and delivery of an agentic digital assistant that unifies data from across the business — dashboards, knowledge hubs, data platforms and trackers — into a single, conversational consumption layer delivered inside Microsoft Teams. Think automated nudges, persona-driven reporting for senior leaders, scenario modelling, and high-risk operational alert support, replacing a sprawl of dashboards with insight on demand.
You'll be embedded in a cross-functional squad from discovery through to production, working alongside solution architects, analysts and business stakeholders.
What you'll do
This is a genuine builder's role. You'll lead the design and delivery of an agentic digital assistant that unifies data from across the business — dashboards, knowledge hubs, data platforms and trackers — into a single, conversational consumption layer delivered inside Microsoft Teams. Think automated nudges, persona-driven reporting for senior leaders, scenario modelling, and high-risk operational alert support, replacing a sprawl of dashboards with insight on demand.
You'll be embedded in a cross-functional squad from discovery through to production, working alongside solution architects, analysts and business stakeholders.
What you'll do
- Architect and build agentic / LLM-driven features that pull, reconcile and synthesise data from multiple sources
- Develop persona-specific views and insight-generation logic (performance nudges, operational suggestions, coaching-content retrieval)
- Build and integrate retrieval / QA pipelines (RAG, vector databases) against existing knowledge systems
- Own LLM model selection, prompt engineering, fine-tuning and evaluation for insight and conversational-query features
- Ensure explainability, accuracy and safety for sensitive, high-risk scenarios
- Help define MLOps, deployment, monitoring and data governance for the product
- Translate stakeholder and persona needs into requirements and support rollout in MS Teams
- 6–10 years as a Data Scientist, operating as a senior individual contributor with end-to-end delivery experience
- Minimum 2–3 years hands-on with Agentic AI — direct experience building agentic, multi-agent or LLM-driven systems, conversational AI, or orchestration layers (essential)
- Strong applied GenAI, RAG and LLM skills: prompt engineering, fine-tuning, retrieval systems, vector databases, connecting to knowledge bases
- Solid ML fundamentals — supervised/unsupervised learning, recommender/insight systems
- Strong coding in Python (pandas, scikit-learn, PyTorch/TensorFlow), APIs and cloud services (AWS preferred)
- Data engineering and integration across multiple sources (SQL, Snowflake, AWS data stack or similar)
- Practical MLOps: deployment, monitoring and versioning
- Strong stakeholder management and comfort working through ambiguous, unstructured problems
- Prior MS Teams integration or conversational-UX experience
- Domain familiarity with financial crime / operational alerting and sensitivity considerations
- Experience migrating or scaling prototypes to production
- Background in human-in-the-loop systems or workflow augmentation for frontline leaders
- Contract engagement based in Sydney (hybrid, proximity to the client squad)
- Availability: immediate joiners through to a 4-week notice period
- You must hold current, unrestricted Australian work rights