AI Engineer

Role: Mid-Level AI Engineer

Function: Data & Analytics / AI Engineering

Location: Remote, India

Type: Full-time

Industry: Mining & Metals

About Company

One of Australia's largest privately owned drilling companies, founded in 2006 in Kalgoorlie-Boulder, Western Australia. The company operates a fleet of 28 purpose-built Schramm and Sandvik rigs.

It specialises in Reverse Circulation (RC) and diamond drilling for Australia's top-tier miners. Services include exploration, grade control, and water bore drilling.

With over 160 employees, the company fosters a culture built on safety, continuous learning, and accountability. It invests in cutting-edge technology and actively supports the mental health and well-being of its people.

Position Overview

This role sits within the Data & Analytics team and carries core engineering responsibility for a growing portfolio of production AI products built on Snowflake. Working from architectures and research validated by the Senior Data Scientist, the engineer independently owns implementation workstreams from detailed design through deployment and ongoing optimisation. The role also provides advanced technical support to the Junior AI Platform Engineer on complex integrations, data services, and production diagnostics.

Role & Responsibilities

• Translate approved reference architectures, security patterns, and non-functional requirements into maintainable production solutions, escalating design gaps or material trade-offs to the Senior Data Scientist.
• Build, release, and support technically complex Snowflake AI solutions across the portfolio — including the RigIQ platform, Lost Opportunity Agent, SNP agents, and future Cortex-based agents — ensuring governed data, integrations, orchestration, and production controls.
• Implement and maintain the shared technical foundation and product services underpinning RigIQ, Driller360, Asset360, and Contract360, covering data ingestion, operational evidence services, APIs, workflow components, and reusable platform capabilities.
• Convert validated ML experiments and research prototypes into tested, repeatable, and supportable production workflows without owning the underlying research agenda.
• Build, test, deploy, and optimise Snowflake Cortex agents that leverage governed enterprise data, semantic models, tools, and retrieval services in line with defined architecture.
• Implement LLMOps and ML pipeline practices covering data preparation, retrieval, feature generation, inference, evaluation, versioning, release management, monitoring, and feedback loops.
• Produce maintainable, version-controlled code; build APIs and integrations; contribute to CI/CD; implement automated testing, logging, tracing, model-health monitoring, and incident diagnostics; and support the Junior AI Platform Engineer on advanced production issues.

Must Have Criteria

• 3–5 years in AI engineering, machine learning engineering, data engineering, or software engineering, with demonstrated delivery and support of production AI, data, or software products.
• Strong Python and SQL skills with hands-on Snowflake experience across data preparation, secure data access, orchestration, performance tuning, and integration with external services.
• Practical experience with LLM applications, RAG pipelines, agent workflows, tool use, prompt management, evaluation datasets, and production quality controls.
• Proven experience with version control, CI/CD, automated testing, model or prompt evaluation, monitoring, tracing, release management, and production incident troubleshooting for LLM or ML services.
• Ability to design and operate reliable LLM or ML pipelines, APIs, and integrations covering data preparation, inference, evaluation, deployment, feedback loops, and ongoing support.
• Working knowledge of observability, data-quality controls, model-health monitoring, security, scalability, incident management, and technical documentation for business-critical services.
• Ability to independently manage a defined technical workstream, communicate engineering trade-offs clearly, and document reusable patterns.

Nice to Have

• Hands-on experience with Snowflake Cortex Agents, Cortex Search, Cortex Analyst, Snowpark, or related Snowflake AI and orchestration capabilities.
• Experience with LLM evaluation and observability tooling, model or prompt registries, and automated quality or regression testing frameworks.
• Experience operationalising traditional ML pipelines including feature pipelines, model deployment, drift monitoring, and retraining workflows.
• Experience delivering AI systems under formal governance, sensitive data constraints, human-review requirements, or strict cost and reliability controls.

What We Offer

• A pivotal engineering role in building the company's production AI capability from the ground up, with direct impact on flagship products like RigIQ.
• Exposure to a modern, governed enterprise AI stack spanning Snowflake, Azure AI services, Microsoft Copilot, and Power Platform.
• Real career development within a growing Data & Analytics team, working alongside experienced data scientists and platform engineers.
• A people-first culture that prioritises safety, mental health, continuous learning, and strong team connection.