In-Demand Program manager, goes infrastructure pmo Jobs in Greenacre, Nsw

FSAI Labs Program Lead
Future Secure AI —Sydney NSW
- Azure
- Doctoral degree
- Research
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FSAI Labs Program Lead

Future Secure AI
Sydney NSW

Quick Apply

Job Details

Qualifications

Data modelling
Azure
Law
Doctoral degree
Google Cloud Platform
Research
Master's degree
AWS
Bachelor's degree
Machine learning
Natural language processing
AI
Leadership
Communication skills

Full Job Description

About the Company

At Future Secure AI, we're building something genuinely new — and we're looking for people bold enough to build it with us. We work at the frontier of AI, tackling big, real-world problems for global enterprises across multiple industries, armed with state-of-the-art technology and a culture that prizes courage, rigor, and relentless curiosity. Our BRAVER values aren't just words on a wall — they describe the kind of people we are and the standard we hold ourselves to every day. Our leadership team is entrepreneurial, experienced, and accessible, with an open-door policy that means you'll never be just a number here. We invest seriously in your growth because we know our success depends on yours. If you're ready to work alongside some of the brightest minds in the industry, push into uncharted territory, and do work that genuinely matters, Future Secure AI is the place for you.

As FSAI Labs Program Lead, you will be the operational engine behind FSAI's applied research function. You will sit at the intersection of research and engineering, translating cutting-edge AI and machine learning work into structured, deliverable programmes that reach enterprise clients at scale.

This is a role for someone who understands how research actually works — the ambiguity, the iteration, the pivots — and who knows how to build the delivery rigour around it without killing the creative process.

Responsibilities

Research Programme Leadership: Own the end-to-end delivery of complex, cross-functional research programmes — from problem framing and hypothesis definition through to deployment — ensuring alignment with company strategy, client commitments, and research integrity
Roadmap and Planning: Define and maintain research delivery roadmaps in close collaboration with research scientists, engineering, and product leadership, sequencing work to balance exploratory investigation with commitments to ship
Cross-Functional Orchestration: Connect and coordinate research scientists, ML engineers, product managers, and client delivery teams, ensuring findings translate into working systems rather than sitting in notebooks
Risk & Dependency Management: Anticipate where research timelines are at risk — model performance, data availability, infrastructure constraints — and drive resolution before they become blockers
Process & Efficiency: Build lightweight, research-appropriate delivery processes that bring accountability and predictability without imposing rigid structure on inherently iterative work
Stakeholder Communication: Translate research progress, setbacks, and findings into clear, honest updates for technical and non-technical stakeholders, including at executive level
Strategic Influence: Shape how the organisation prioritises research investment, drawing on delivery data, client feedback, and emerging capability to inform what gets built next
Team Enablement: Give research teams the tooling, process support, and operational clarity they need to focus on the work that matters
Research-to-Product Pipeline: Manage the critical handoff between research output and production AI systems, ensuring models and findings are integrated into the core platform in a governed, scalable way

Minimum Qualifications

Bachelor's degree in Mathematics, Data Science, Statistics, Computer Science or a related discipline
10+ years of experience delivering complex technical or research programmes, with at least 3 years in an AI or ML environment
Strong Data Science domain knowledge
Demonstrated ability to manage delivery in ambiguous, research-driven contexts where outputs are uncertain and timelines are estimates
Strong grasp of software development and research methodologies — Agile, Scrum, and the ability to adapt when standard frameworks don't fit
Exceptional communication skills, with the ability to hold a room with research scientists and C-suite stakeholders equally
Analytical and data-driven in how you make decisions and report progress
Experience with LLM-driven research or applied NLP is highly preferred
Familiarity with cloud platforms (AWS, Azure, or GCP)

Preferred Qualifications

Master's degree or Doctorate in Data Science, Mathematics, Statistics, Computer Science or a related discipline
Hands-on familiarity with modern LLM ecosystems — LangChain, LlamaIndex, RAG pipelines, multi-agent frameworks, or GPT-based systems
Working knowledge of NLP techniques: transformers, topic modelling, supervised NLP, LSTMs
Python experience in a machine learning or research context
Prior experience as a research engineer, ML scientist, or technical lead before moving into delivery

Why Join Us?

A high-performance culture
State-of-the-art technology
Experience world-class leadership
Scale of impact and purpose
A competitive salary and a huge growth trajectory
Work with the best in the industry
Flexible work environment
Diversity and creativity

Disclaimer: We do not wish to be contacted by recruitment agencies. Our hiring process is managed in-house and the best way for candidates to express interest is by applying with your resume through our company website.

Future Secure AI Privacy Policy

At Future Secure AI, we are committed to protecting your privacy and adhering to the principles of the General Data Protection Regulation (GDPR) and the Australian Privacy Principles (APPs) under the Privacy Act 1988 (Cth). Our Privacy Policy outlines how we collect, use, share, and protect your personal data when you visit our website at www.futuresecure.ai (the "Website") and use our services.

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