AI Engineer - Full Stack

1. AI tool development and automation Design, build and deploy AI-powered tools that address real operational needs across Client Company’s engineering, environmental and geospatial disciplines. The near-term opportunity register is specific and commercially prioritised — initial focus areas include: • Document revision cycle automation: processing client comment registers, mapping comments to drawings and documents, and generating action lists — the highest-identified source of rework cost on fixed-fee projects. • Proposal and RFQ generation: AI-assisted first-draft generation from historical proposals and project deliverables, directly reducing the largest source of non-billable BD cost across the business. • Environmental and ecology report drafting: RAG-based tools for AUSTRALIA ENVIRONMENT CONSULTING and AUSTRALIA HERITAGE CONSULTING teams to accelerate structured report generation (BDARs, flora/fauna assessments, heritage impact statements) on fixed-fee contracts. • Engineering workflow tools: instrument datasheet auto-population, LV cable sizing automation, process control narrative drafting, P&ID and drawing validation, Caesar II and SpaceGass results extraction, AS2885 compliance checking. • Go-by library consolidation and AI-powered search: foundational data infrastructure underpinning most other tools. Develop and maintain integrations within the Microsoft 365 / Azure ecosystem: Power Automate, Azure OpenAI, Copilot Studio and related services. Build Python-based tools and scripts for data-heavy workflows. Ensure all tools are robust, documented and maintained — not one-off prototypes that create technical debt. 2. Workflow analysis and improvement Conduct structured audits of existing workflows across engineering, environmental, survey and energy disciplines to identify and prioritise automation opportunities. Client Company’s primary pain points are well-understood: document revision cycles (rework cost), proposal effort that does not convert (non-billable cost), and fee pressure in engineering delivery (margin squeeze). Work with discipline leads to translate domain-specific requirements into working solutions, and measure and report the commercial impact of deployed tools. 3. AI adoption and capability uplift Design and deliver practical AI training tailored to Client Company’s specific disciplines — process engineers, environmental scientists, GIS specialists and pipeline engineers have different task types and different starting points. Develop discipline-specific playbooks with ready-to-use prompt templates for common deliverables. Act as the internal go-to for AI questions. Champion responsible use including output validation, model limitations and data governance. 4. Data infrastructure and geospatial processing Lead the consolidation of Client Company’s go-by and template library from scattered shared drives into a structured, searchable SharePoint index — this is foundational infrastructure for the RAG-based tools above. Support automation of survey data processing pipelines, point cloud and field data workflows, and geospatial QA automation. Identify and apply ML and LLM approaches to structured and unstructured engineering datasets. 5. Stakeholder engagement and HVC coordination Engage with internal teams to understand problems before building solutions. Work closely with the Philippines-based HVC (high value centre) team, whose workflow tools are a primary focus of the engineering automation agenda. Collaborate across Brisbane, Melbourne and Manila offices. Occasionally interface with clients where Client Company’s AI capability is a commercial differentiator.

Pay: ₹383,000.00 - ₹1,650,000.00 per year

Work Location: Remote