MLOps / LLMOps Engineer (Mid-Level)

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

• Operationalize the complete ML lifecycle—including

—on Databricks.

• Implement ML workflows using the

with Delta Lake as the underlying storage layer.

• Establish implementation patterns for

, 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.

• 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

.

• Implement vector search and retrieval architectures using technologies such as

.

• 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

patterns based on business and latency requirements.

• Establish clear service-level expectations and operational SLAs for Priority A/B/C workloads.

• Integrate

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

.

• Ensure regulated or sensitive Bronze-layer data remains in the required geographic region.

• Ensure only appropriately anonymized or aggregated data is transferred to global workspaces or services.

• Maintain end-to-end lineage across the ML lifecycle, including:

• Surface lineage and governance information through appropriate monitoring and governance dashboards.

• Support security, compliance, audit, and access-review requirements across the ML platform.

• Use

to version, test, and promote ML/LLM assets across environments.

• Automate promotion across:

• Ensure production changes are deployed through controlled CI/CD processes with

.

• 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

before production publication.

• Establish deployment gates that prevent models or applications from progressing when quality, security, or performance requirements are not met.

• Instrument ML/LLM pipelines and services to support defined SLOs, including:

•

≤60 minutes

• Implement proactive monitoring, alerting, and operational dashboards.

• Integrate automated

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

.

• Participate in or establish appropriate

for critical ML/LLM services.

• Develop and maintain disaster-recovery procedures aligned with tiered

RPO ≤15 minutes / RTO ≤2 hours

• Participate in DR testing and document recovery outcomes and remediation actions.

• 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

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.

• 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

.

• Data Scientists and Product teams can leverage standardized MLOps/LLMOps patterns rather than building bespoke deployment and monitoring solutions.

in MLOps, LLMOps, ML Engineering, Data Engineering, or platform-focused ML engineering.

• Hands-on experience with

, including:

• Databricks Jobs and Workflows

• Delta Lake

• Unity Catalog

• Databricks SQL Warehouses

• Proven experience building and maintaining

using:

• GitHub Actions

• Databricks Asset Bundles (DABs)

• Environment promotion across

• Parameterized deployments

• Secure secrets management using

, or equivalent technologies

• Strong understanding of

.

• Experience implementing Great Expectations-style validation frameworks or equivalent rule-based data-quality solutions.

• Experience building

, 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

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

.

• 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.

• Hands-on experience with

, 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

, including technologies and concepts such as:

• PostGIS

• Spatial joins

• Spatial indexing and tiling

• Coordinate systems and projections

• GIS-based feature engineering

• Experience integrating

, including an understanding of:

• Dataset refresh SLAs

• Query concurrency

• Row-Level Security (RLS)

• Object-Level Security (OLS)

• Practical knowledge of

, including:

• Resource tagging

• Budget management

• Cost monitoring

• Showback/chargeback

• Cost anomaly detection and alerting

• Knowledge of

, including:

• Delta Lake Deep Clone

• Delta Sharing

• Cross-region recovery

• Tiered RTO/RPO strategies

• DR testing and evidence collection

• Hands-on experience with

, 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.

• Experience deploying and operating models supporting

.

• Experience building CI/CD workflows that promote

, with automated data contracts and quality gates.

• Experience implementing model and data observability for workloads using

.

• Familiarity with monitoring:

• Data and concept drift

• Model performance

• SLOs

• Pipeline health

• Alerting and incident management

• Understanding of

• Reliable promotion of ML/LLM workloads through

using automated CI/CD.

• Consistent implementation of data contracts, validation rules, security controls, and governance requirements.

• Production pipelines meeting defined

.

• 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

.

• 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.