MLOps / LLMOps Engineer (Mid-Level)

Irth Solutions is a leading provider of cloud-based SaaS software for damage prevention, asset integrity, stakeholder engagement and land management, helping energy, utility, telecom, and infrastructure companies protect their critical network infrastructure. With nearly three decades of industry experience, Irth serves customers across North America and continues to expand its platform with new data-driven and AI-powered capabilities.

MLOps / LLMOps Engineer – Insights (AI/ML)

Qualifications

Required Qualifications

• 3–5 years of experience in MLOps, LLMOps, ML Engineering, Data Engineering, or platform-focused ML engineering.
• Hands-on experience with Databricks, including:
• Databricks Jobs and Workflows
• Delta Lake
• Unity Catalog
• Databricks SQL Warehouses
• Proven experience building and maintaining CI/CD pipelines for data and ML workloads using:
• GitHub Actions
• Databricks Asset Bundles (DABs)
• Environment promotion across DEV → QA → PROD
• Parameterized deployments
• Secure secrets management using Azure Key Vault (AKV), AWS KMS/Secrets Manager, or equivalent technologies
• Strong understanding of data contracts, schema governance, and automated data/feature validation.
• Experience implementing Great Expectations-style validation frameworks or equivalent rule-based data-quality solutions.
• Experience building observable production pipelines, including metrics, dashboards, alerting, and monitoring against defined SLOs such as:
• Pipeline success rate
• Data freshness
• Mean Time to Detect (MTTD)
• Mean Time to Repair (MTTR)
• Strong security-first mindset with practical experience in:
• RBAC/ABAC
• Unity Catalog security
• PII detection and obfuscation
• Private networking
• Data-access controls
• Policy-as-code for data residency
• Strong proficiency in Python and SQL.
• Working knowledge of distributed computing and job orchestration within Databricks/Spark environments.
• Ability to troubleshoot production ML/data workloads and participate in operational support and incident resolution.

Preferred Qualifications

• Hands-on experience with LLM/GenAI workflows, including:
• Prompt engineering
• Retrieval-Augmented Generation (RAG)
• LLM evaluation frameworks and evaluation harnesses
• AI safety and guardrails
• Retrieval and response-quality evaluation
• Latency optimization
• Token and API-cost optimization
• Experience with geospatial data and analytics, including technologies and concepts such as:
• PostGIS
• Spatial joins
• Spatial indexing and tiling
• Coordinate systems and projections
• GIS-based feature engineering
• Experience integrating Power BI with Databricks SQL Warehouses and semantic layers, including an understanding of:
• Dataset refresh SLAs
• Query concurrency
• Row-Level Security (RLS)
• Object-Level Security (OLS)
• Practical knowledge of FinOps, including:
• Resource tagging
• Budget management
• Cost monitoring
• Showback/chargeback
• Cost anomaly detection and alerting
• Knowledge of Databricks disaster-recovery patterns, including:
• Delta Lake Deep Clone
• Delta Sharing
• Cross-region recovery
• Tiered RTO/RPO strategies
• DR testing and evidence collection
• Hands-on experience with Microsoft Azure and AWS, particularly where ML and data workloads span both environments.
• Understanding of cloud-native security patterns, including:
• Private Link
• VPC/VNet connectivity and peering
• Egress restrictions
• KMS
• AWS Secrets Manager
• Azure Key Vault
• Data-plane isolation
• Ability to work effectively across cloud, platform, data, ML, security, and product teams.

Nice-to-Have Qualifications

• Experience deploying and operating models supporting excavation risk scoring, asset integrity, anomaly detection, or predictive maintenance.
• Experience building CI/CD workflows that promote asset-integrity or infrastructure-risk models across DEV → QA → PROD, with automated data contracts and quality gates.
• Experience implementing model and data observability for workloads using pipeline inspection, sensor, maintenance, or asset-condition data streams.
• Familiarity with monitoring:
• Data and concept drift
• Model performance
• SLOs
• Pipeline health
• Alerting and incident management
• Understanding of data residency, security, privacy, compliance, and disaster-recovery requirements for asset-integrity, pipeline, utility, or infrastructure data used by ML services.

Success Metrics

Success in this role will be measured by the engineer’s ability to establish reliable, repeatable, and secure MLOps/LLMOps practices across the enterprise platform.

Key measures include:

• Reliable promotion of ML/LLM workloads through DEV → QA → PROD using automated CI/CD.
• Consistent implementation of data contracts, validation rules, security controls, and governance requirements.
• Production pipelines meeting defined availability, freshness, MTTD, and MTTR SLOs.
• Strong observability across data, features, models, LLM applications, and serving infrastructure.
• Reduced production incidents through proactive monitoring, automated testing, and standardized deployment patterns.
• Effective management and optimization of ML/LLM infrastructure and inference costs.
• Demonstrated compliance with security, residency, lineage, and DR requirements.
• Reusable MLOps/LLMOps patterns that enable Data Science and Product teams to deploy new AI capabilities faster and more safely.
• Strong collaboration with Data Science, Data Engineering, Architecture, Security, Product, and domain teams.