Role Description As an MLOps / LLMOps Engineer, you will design, automate, and operate scalable ML and LLM systems on Irth’s enterprise Lakehouse platform. You will work closely with Data Science, Engineering, and Product teams to deploy reliable, secure, and production-ready ML and GenAI solutions. This role focuses on operationalizing ML models, building CI/CD pipelines, ensuring governance and compliance, and maintaining high-performance, observable AI systems. Qualifications - 3–5 years of experience in MLOps, LLMOps, or ML platform engineering roles. - Hands-on experience with Databricks, Delta Lake, Unity Catalog, and ML deployment workflows. - Strong experience with CI/CD pipelines using GitHub Actions and infrastructure automation. - Experience implementing data quality validation, schema governance, and data contracts. - Experience building production-grade ML pipelines with monitoring and observability. - Strong security knowledge including RBAC, encryption, data residency, and governance practices. - Proficiency in Python, SQL, and distributed data processing frameworks. Requirements - Deploy and manage ML and GenAI solutions including risk scoring, anomaly detection, predictive maintenance, NLP, and RAG pipelines. - Build and optimize LLM pipelines using vector databases, model serving endpoints, and inference workflows. - Optimize models using quantization, caching, and performance tuning techniques. - Implement batch and real-time inference pipelines with defined SLAs. - Implement data contracts, schema validation, and data quality checks across ML pipelines. - Ensure secure handling of sensitive data including PII detection, classification, and obfuscation. - Maintain full lineage from data sources to deployed models and serving endpoints. - Enforce data residency, governance, and compliance policies. - Implement CI/CD pipelines using GitHub Actions and Databricks Asset Bundles. - Automate deployments across DEV, QA, and PROD environments. - Develop unit and integration tests for data pipelines and ML models. - Ensure version control, reproducibility, and automated deployment workflows. - Monitor pipeline health, model performance, drift, and system reliability. - Implement alerting, incident response workflows, and automated ticketing. - Track LLM performance metrics including latency, hallucination rates, and API costs. - Develop runbooks, disaster recovery procedures, and operational documentation. - Apply tagging policies and cost tracking for ML infrastructure. - Support budget monitoring, cost optimization, and resource management. Benefits - Being an integral part of a dynamic, growing company that is well respected in its industry. - Competitive pay based on experience. Company Description Irth Solutions is a software product company building cutting-edge technology platforms that enable data-driven insights across Damage Prevention, Asset Integrity, Land Management, and Stakeholder Engagement. With a strong product culture, collaborative environment, and high growth potential, Irth offers opportunities to work on enterprise-scale data and AI platforms.

• utilize in-house and 3rd party LLMs to solve language understanding problems • employ techniques such as fine-tuning and RAG to improve models • contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development • help drive optimization, testing, and tooling to improve quality of our content enrichment assets • collaborate with cross-functional teams of MLEs, data and backend engineers, and other stakeholders including tech research, data science, and product to develop new features and technologies • be a participant in our AI Foundation’s ML community and work collaboratively and efficiently within our existing platforms and systems • perform data analysis to establish baselines and inform product decisions • stay up-to-date on the latest machine learning algorithms and techniques

Role Description We're looking for a Machine Learning Engineer to join our Insurance AI team. You'll be the engineering backbone for our Data Scientists, building and maintaining the ML infrastructure that turns models into reliable, scalable products. This isn't a greenfield build-everything-from-scratch role. Our Sydney-based AI & Computer Vision team has built robust ML tooling and pipelines. Your job is to extend, adapt, and maintain that infrastructure for US-specific use cases. If you're someone who gets satisfaction from making existing systems work better rather than reinventing the wheel, keep reading. You'll work closely with Data Scientists in the US and ML Engineers in Australia, acting as the technical bridge that keeps both teams moving fast. What You'll Do - Own the ML engineering function for the US Insurance AI team. - Build data and model pipelines, integrating with internal and external APIs. - Ensure Data Scientists have the tools they need to ship models to production. - Collaborate daily with our Sydney AICV team to leverage shared infrastructure and contribute improvements back. - Write Python, wrangle data pipelines, debug production issues, and translate Data Scientist requirements into working systems. - Use AWS, work with cloud-native technologies, and operate within an established MLOps framework. Key Responsibilities - Build and maintain ML pipelines for data ingestion, feature processing, model training, deployment, and monitoring in AWS. - Extend and adapt existing tooling from our Sydney AICV team for US Insurance AI use cases. - Develop and support internal tools and frameworks that streamline experimentation and improve delivery speed. - Integrate internal and external APIs to connect datasets, models, and services. - Partner with Data Scientists to understand their workflow needs and translate them into scalable technical solutions. - Ensure infrastructure supports rapid experimentation while maintaining reliability, security, and scalability. - Collaborate with Technical Product Managers, API engineers, and platform teams to deploy models in production. - Contribute to a shared codebase through feature branches, pull requests, and code reviews. Qualifications - 2-4 years as a Machine Learning Engineer or ML-focused Software Engineer. - Strong Python skills with a track record of writing clean, tested, production-grade code. - Hands-on experience with ML libraries like PyTorch, scikit-learn, and pandas. - Experience building and maintaining ML pipelines in production environments. - Solid SQL skills and familiarity with data engineering tools (Airflow, Spark, or dbt). - The ability to jump into an existing codebase, understand it, and extend it. - Clear communication skills and comfort working across time zones. Requirements - AWS experience (S3, EC2, ECS, or similar). - Experience consuming and integrating REST APIs at scale. - Docker and containerisation experience. - MLOps experience including CI/CD and model monitoring. - Familiarity with geospatial or aerial imagery data. - Experience with pipeline orchestration tools like Ray, Kubeflow, or Flyte. Benefits - 4 extra "YOU" days off each year—take a break, no questions asked! - Company-sponsored volunteering days to give back. - Generous parental leave policies for growing families. - Work from Overseas Policy - explore the world in the approved list of cities while you work! - Access to LinkedIn Learning for continuous growth. - Discounted Private Health Insurance plans. - Monthly wellbeing and technology allowance. - A Nearmap subscription (naturally!).

Role Description - Writing effective, scalable code. - Knowledge of OpenCV. - Knowledge of machine learning libraries e.g. Tensorflow, Keras, Numpy, Matplotlib, Pandas, Dask etc. - Knowledge of data analysis in Python, Predictive analysis, Correlation, Regression, Factor Analysis, NLP etc. - Knowledge of data modelling, categorisation. - Knowledge of image classification, object detection, recommending system. Qualifications - 5+ years of experience as Python Developer/Python Engineer. - Strong Problem solving ability. - Experience handling gigabyte and terabyte size datasets. - You are & remain aware of the latest tools and technologies. - Experience working with retail or e-commerce data. - Experience in Python scripting. - Object-oriented programming skills. - Experience with version control systems (Git). - Experience deploying machine learning models.