MLOps / LLMOps Engineer (Mid-Level)

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) Location: Remote – India Department: Insights (AI/ML) Reports to: Data Platform & Analytics Manager 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.

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 online inference patterns based on business and latency

. Establish clear service-level expectations and operational SLAs for Priority A/B/C workloads. 3.

Engineer Reliability, Security & Compliance into the ML Lifecycle Integrate 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 only 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 Use 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 Instrument ML/LLM pipelines and services to support defined SLOs, including: 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