About Irth Solutions
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)
About the Role
Irth is building a governed, multi-cloud
Lakehouse on Databricks
to unlock cross-product insights, enforce data residency, and accelerate AI/ML innovation for our customers.
We are looking for an
MLOps/LLMOps Engineer
to translate this foundation into scalable, automated, secure, and observable machine learning and LLM services.
You will work closely with Data Science, Data Engineering, Platform, Product, and domain teams to productionize ML and GenAI capabilities supporting Irth’s key industries:
- Damage Prevention
- Asset Integrity
- Land Management
- Stakeholder Engagement
This is a pivotal role in establishing reusable engineering patterns for
data contracts, lineage, data quality, security, CI/CD, model deployment, monitoring, and operational reliability
.
You will help ensure that models and LLM applications move efficiently from experimentation into production—and remain reliable, observable, secure, and cost-effective throughout their lifecycle.
Key Responsibilities
1. Build the ML/LLM Platform on the Lakehouse
- Operationalize the complete ML lifecycle—including
training, evaluation, packaging, deployment, and monitoring
—on Databricks.
- Implement ML workflows using the
Bronze → Silver → Gold medallion architecture
with Delta Lake as the underlying storage layer.
- Establish implementation patterns for
Unity Catalog model management
, preparing model assets for catalog-based governance, lineage, discovery, and access control.
Develop reusable templates for ML/LLM jobs, workflows, and deployment processes.
Create and maintain cluster policies for ML/LLM workloads aligned with enterprise platform guardrails.
Apply platform standards such as:
- Private networking
- Mandatory resource tagging
- Long-Term Support (LTS) Databricks Runtime versions
- Secure secrets management
- Appropriate compute policies
Establish reusable patterns that allow Data Scientists and ML Engineers to deploy models consistently and safely.
2. Productionize ML & LLM Features
Partner with Data Science and Product teams to productionize models supporting use cases such as:
- Excavation and infrastructure risk scoring
- Anomaly detection
- Predictive maintenance
- Geospatial enrichment
- Named Entity Recognition (NER) over parcels and easements
- Stakeholder communication summarization
- Retrieval-Augmented Generation (RAG)
- AI-powered assistants and decision-support applications
Design, build, and maintain production-grade
LLM and RAG pipelines
.
- Implement vector search and retrieval architectures using technologies such as
Databricks Vector Search
.
Deploy and manage model-serving and inference endpoints.
Optimize inference workloads for performance, scalability, reliability, and cost.
Apply optimization techniques such as:
- Quantization
- Distillation
- Prompt and response caching
- Retrieval optimization
- Batching
Implement
batch, streaming, and inference
patterns based on business and latency requirements.
- Establish clear service-level expectations and operational SLAs for Priority A/B/C workloads.
3. Engineer Reliability, Security & Compliance into the ML Lifecycle
data contracts and quality gates
into ML and LLM pipelines.
Implement automated validation for:
- Schema drift
- Null thresholds
- Duplicate records
- Referential integrity
- Data completeness
- Feature-quality issues
Implement PII detection, classification, masking, and obfuscation before sensitive data is consumed by features or models.
Enforce
data residency requirements through policy-as-code
.
- Ensure regulated or sensitive Bronze-layer data remains in the required geographic region.
- Ensure appropriately anonymized or aggregated data is transferred to global workspaces or services.
- Maintain end-to-end lineage across the ML lifecycle, including:
Source tables/columns → Features → Models → Serving Endpoints → Applications/BI
- Surface lineage and governance information through appropriate monitoring and governance dashboards.
- Support security, compliance, audit, and access-review requirements across the ML platform.
4. Automate Everything – CI/CD & Testing
Databricks Asset Bundles (DABs)
and
GitHub Actions
to version, test, and promote ML/LLM assets across environments.
- Automate promotion across:
DEV → QA → PROD
- Ensure production changes are deployed through controlled CI/CD processes with
no direct development in PROD
.
Version and manage:
- Jobs
- Notebooks
- Model artifacts
- Cluster policies
- Configuration
- Permissions
- Deployment definitions
Build automated unit, integration, regression, and data-quality test suites.
Implement model-quality validation as part of deployment pipelines.
Validate business KPIs and analytical outputs against the
Unity Catalog semantic layer
before production publication.
- Establish deployment gates that prevent models or applications from progressing when quality, security, or performance requirements are not met.
5. Observability & Production Operations
P1 pipeline success: ≥99.5%
P1 MTTD: ≤5 minutes
- MTTR: ≤60 minutes
- Implement proactive monitoring, alerting, and operational dashboards.
- Integrate automated
Jira ticket creation
for qualifying P1 production failures.
Monitor ML systems for:
- Model performance degradation
- Data drift
- Concept drift
- Feature-quality degradation
- Prediction distribution changes
Establish LLM-specific observability, including:
- Hallucination rates
- Response quality
- Latency
- Token consumption
- API usage
- Inference costs
- Retrieval quality
Define appropriate thresholds and automated alerts for model and LLM quality degradation.
Develop and maintain production
runbooks, troubleshooting procedures, and operational documentation
.
- Participate in or establish appropriate
on-call rotations
for critical ML/LLM services.
- Develop and maintain disaster-recovery procedures aligned with tiered
RTO/RPO objectives
, including examples such as:
- Tier 1: RPO ≤15 minutes / RTO ≤2 hours
- Participate in DR testing and document recovery outcomes and remediation actions.
6. FinOps & Cost Management
Enforce mandatory cost and ownership tags across ML/LLM infrastructure, including:
- Domain
- Tenant
- Environment
- Cost center
- Owner
Ensure tags are consistently propagated across jobs, clusters, warehouses, and other applicable resources.
Support
showback and chargeback
reporting for ML/LLM workloads.
- Monitor compute, storage, model-serving, and LLM/API costs.
- Identify opportunities to optimize infrastructure and inference costs without compromising service quality or SLOs.
- Establish cost visibility and budget controls for production AI workloads.
- Detect and investigate abnormal cost increases or inefficient workloads.
Role Outcomes
In this role, you will help establish the engineering foundation that allows Irth to move from
ML/LLM experimentation to reliable production AI at scale
.
Success means that:
- Models and LLM applications can be deployed through repeatable, automated CI/CD processes.
- ML/LLM workloads are secure, governed, observable, and production-ready.
- Data, feature, model, and serving lineage is traceable end-to-end.
- Production services consistently meet defined reliability and performance SLOs.
- Model and LLM quality degradation is detected before it materially impacts customers.
- AI workloads are optimized for both
business value and infrastructure/API cost
.
- Data Scientists and Product teams can leverage standardized MLOps/LLMOps patterns rather than building bespoke deployment and monitoring solutions.
Requirements
Qualifications
Required Qualifications
3–5 years of experience
in MLOps, LLMOps, ML Engineering, Data Engineering, or platform-focused ML engineering.
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
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
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
.
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.
Benefits
Benefits
Competitive Salary – A competitive compensation package based on experience and qualifications.
Medical, Dental, and Vision Insurance – Comprehensive insurance coverage to support you and your family.
401(k) Plan with Company Match.
Generous Paid Time Off (PTO) – Time off to support work-life balance and personal needs.
Company-Paid Holidays – Paid holidays throughout the year.
Flexible Work Options – Work-from-home opportunities are available, depending on role and business needs.
On-Call Compensation – Additional pay for eligible on-call shifts.
