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. 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 online 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 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 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. 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. 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.