{"id":577101,"url":"https://alion.io/job/irth-solutions-mlops-llmops-engineer-mid-level","title":"MLOps / LLMOps Engineer","company":{"id":7550,"name":"Irth Solutions","domain":"irthsolutions.com","url":"https://alion.io/company/irth-solutions","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":60,"computed_at":"2026-10-27T05:46:45Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":42000,"period":"year","method":"role_seniority_country_cell","sample_n":39},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"FinOps","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Great Expectations","optional":false},{"name":"Hallucination","optional":false},{"name":"Jira","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"NER","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Incident Management","optional":true},{"name":"PostGIS","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Power BI","optional":true},{"name":"Prompt Engineering","optional":true}],"status":"live","first_seen_at":"2026-09-09T00:00:00Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-27T20:16:00Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":48},"description":"About Irth Solutions\nIrth 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.\nMLOps / LLMOps Engineer - Insights (AI/ML)\nLocation: Remote - India\nDepartment: Insights (AI/ML)\nReports to: Data Platform & Analytics Manager\nAbout the Role\nIrth 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.\nWe are looking for an MLOps/LLMOps Engineer to translate this foundation into scalable, automated, secure, and observable machine learning and LLM services.\nYou will work closely with Data Science, Data Engineering, Platform, Product, and domain teams to productionize ML and GenAI capabilities supporting Irth’s key industries:\nDamage Prevention\nAsset Integrity\nLand Management\nStakeholder Engagement\nThis is a pivotal role in establishing reusable engineering patterns for data contracts, lineage, data quality, security, CI/CD, model deployment, monitoring, and operational reliability.\nYou 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.\nKey Responsibilities\n1. Build the ML/LLM Platform on the Lakehouse\nOperationalize the complete ML lifecycle-including training, evaluation, packaging, deployment, and monitoring -on Databricks.\nImplement ML workflows using the Bronze → Silver → Gold medallion architecture with Delta Lake as the underlying storage layer.\nEstablish implementation patterns for Unity Catalog model management, preparing model assets for catalog-based governance, lineage, discovery, and access control.\nDevelop reusable templates for ML/LLM jobs, workflows, and deployment processes.\nCreate and maintain cluster policies for ML/LLM workloads aligned with enterprise platform guardrails.\nApply platform standards such as:\nPrivate networking\nMandatory resource tagging\nLong-Term Support (LTS) Databricks Runtime versions\nSecure secrets management\nAppropriate compute policies\nEstablish reusable patterns that allow Data Scientists and ML Engineers to deploy models consistently and safely.\n2. Productionize ML & LLM Features\nPartner with Data Science and Product teams to productionize models supporting use cases such as:\nExcavation and infrastructure risk scoring\nAnomaly detection\nPredictive maintenance\nGeospatial enrichment\nNamed Entity Recognition (NER) over parcels and easements\nStakeholder communication summarization\nRetrieval-Augmented Generation (RAG)\nAI-powered assistants and decision-support applications\nDesign, build, and maintain production-grade LLM and RAG pipelines.\nImplement vector search and retrieval architectures using technologies such as Databricks Vector Search.\nDeploy and manage model-serving and inference endpoints.\nOptimize inference workloads for performance, scalability, reliability, and cost.\nApply optimization techniques such as:\nQuantization\nDistillation\nPrompt and response caching\nRetrieval optimization\nBatching\nImplement batch, streaming, and online inference patterns based on business and latency requirements.\nEstablish clear service-level expectations and operational SLAs for Priority A/B/C workloads.\n3. Engineer Reliability, Security & Compliance into the ML Lifecycle\nIntegrate data contracts and quality gates into ML and LLM pipelines.\nImplement automated validation for:\nSchema drift\nNull thresholds\nDuplicate records\nReferential integrity\nData completeness\nFeature-quality issues\nImplement PII detection, classification, masking, and obfuscation before sensitive data is consumed by features or models.\nEnforce data residency requirements through policy-as-code.\nEnsure regulated or sensitive Bronze-layer data remains in the required geographic region.\nEnsure only appropriately anonymized or aggregated data is transferred to global workspaces or services.\nMaintain end-to-end lineage across the ML lifecycle, including:Source tables/columns → Features → Models → Serving Endpoints → Applications/BI\nSurface lineage and governance information through appropriate monitoring and governance dashboards.\nSupport security, compliance, audit, and access-review requirements across the ML platform.\n4. Automate Everything - CI/CD & Testing\nUse Databricks Asset Bundles (DABs) and GitHub Actions to version, test, and promote ML/LLM assets across environments.\nAutomate promotion across:DEV → QA → PROD\nEnsure production changes are deployed through controlled CI/CD processes with no direct development in PROD.\nVersion and manage:\nJobs\nNotebooks\nModel artifacts\nCluster policies\nConfiguration\nPermissions\nDeployment definitions\nBuild automated unit, integration, regression, and data-quality test suites.\nImplement model-quality validation as part of deployment pipelines.\nValidate business KPIs and analytical outputs against the Unity Catalog semantic layer before production publication.\nEstablish deployment gates that prevent models or applications from progressing when quality, security, or performance requirements are not met.\n5. Observability & Production Operations\nInstrument ML/LLM pipelines and services to support defined SLOs, including:\nP1 pipeline success: ≥99.5%\nP1 MTTD: ≤5 minutes\nMTTR: ≤60 minutes\nImplement proactive monitoring, alerting, and operational dashboards.\nIntegrate automated Jira ticket creation for qualifying P1 production failures.\nMonitor ML systems for:\nModel performance degradation\nData drift\nConcept drift\nFeature-quality degradation\nPrediction distribution changes\nEstablish LLM-specific observability, including:\nHallucination rates\nResponse quality\nLatency\nToken consumption\nAPI usage\nInference costs\nRetrieval quality\nDefine appropriate thresholds and automated alerts for model and LLM quality degradation.\nDevelop and maintain production runbooks, troubleshooting procedures, and operational documentation.\nParticipate in or establish appropriate on-call rotations for critical ML/LLM services.\nDevelop and maintain disaster-recovery procedures aligned with tiered RTO/RPO objectives, including examples such as:\nTier 1: RPO ≤15 minutes / RTO ≤2 hours\nParticipate in DR testing and document recovery outcomes and remediation actions.\n6. FinOps & Cost Management\nEnforce mandatory cost and ownership tags across ML/LLM infrastructure, including:\nDomain\nTenant\nEnvironment\nCost center\nOwner\nEnsure tags are consistently propagated across jobs, clusters, warehouses, and other applicable resources.\nSupport showback and chargeback reporting for ML/LLM workloads.\nMonitor compute, storage, model-serving, and LLM/API costs.\nIdentify opportunities to optimize infrastructure and inference costs without compromising service quality or SLOs.\nEstablish cost visibility and budget controls for production AI workloads.\nDetect and investigate abnormal cost increases or inefficient workloads.\nRole Outcomes\nIn this role, you will help establish the engineering foundation that allows Irth to move from ML/LLM experimentation to reliable production AI at scale.\nSuccess means that:\nModels and LLM applications can be deployed through repeatable, automated CI/CD processes.\nML/LLM workloads are secure, governed, observable, and production-ready.\nData, feature, model, and serving lineage is traceable end-to-end.\nProduction services consistently meet defined reliability and performance SLOs.\nModel and LLM quality degradation is detected before it materially impacts customers.\nAI workloads are optimized for both business value and infrastructure/API cost.\nData Scientists and Product teams can leverage standardized MLOps/LLMOps patterns rather than building bespoke deployment and monitoring solutions.\nRequirements\nQualifications\nRequired Qualifications\n3-5 years of experience in MLOps, LLMOps, ML Engineering, Data Engineering, or platform-focused ML engineering.\nHands-on experience with Databricks, including:\nDatabricks Jobs and Workflows\nDelta Lake\nUnity Catalog\nDatabricks SQL Warehouses\nProven experience building and maintaining CI/CD pipelines for data and ML workloads using:\nGitHub Actions\nDatabricks Asset Bundles (DABs)\nEnvironment promotion across DEV → QA → PROD\nParameterized deployments\nSecure secrets management using Azure Key Vault (AKV), AWS KMS/Secrets Manager, or equivalent technologies\nStrong understanding of data contracts, schema governance, and automated data/feature validation.\nExperience implementing Great Expectations-style validation frameworks or equivalent rule-based data-quality solutions.\nExperience building observable production pipelines, including metrics, dashboards, alerting, and monitoring against defined SLOs such as:\nPipeline success rate\nData freshness\nMean Time to Detect (MTTD)\nMean Time to Repair (MTTR)\nStrong security-first mindset with practical experience in:\nRBAC/ABAC\nUnity Catalog security\nPII detection and obfuscation\nPrivate networking\nData-access controls\nPolicy-as-code for data residency\nStrong proficiency in Python and SQL.\nWorking knowledge of distributed computing and job orchestration within Databricks/Spark environments.\nAbility to troubleshoot production ML/data workloads and participate in operational support and incident resolution.\nPreferred Qualifications\nHands-on experience with LLM/GenAI workflows, including:\nPrompt engineering\nRetrieval-Augmented Generation (RAG)\nLLM evaluation frameworks and evaluation harnesses\nAI safety and guardrails\nRetrieval and response-quality evaluation\nLatency optimization\nToken and API-cost optimization\nExperience with geospatial data and analytics, including technologies and concepts such as:\nPostGIS\nSpatial joins\nSpatial indexing and tiling\nCoordinate systems and projections\nGIS-based feature engineering\nExperience integrating Power BI with Databricks SQL Warehouses and semantic layers, including an understanding of:\nDataset refresh SLAs\nQuery concurrency\nRow-Level Security (RLS)\nObject-Level Security (OLS)\nPractical knowledge of FinOps, including:\nResource tagging\nBudget management\nCost monitoring\nShowback/chargeback\nCost anomaly detection and alerting\nKnowledge of Databricks disaster-recovery patterns, including:\nDelta Lake Deep Clone\nDelta Sharing\nCross-region recovery\nTiered RTO/RPO strategies\nDR testing and evidence collection\nHands-on experience with Microsoft Azure and AWS, particularly where ML and data workloads span both environments.\nUnderstanding of cloud-native security patterns, including:\nPrivate Link\nVPC/VNet connectivity and peering\nEgress restrictions\nKMS\nAWS Secrets Manager\nAzure Key Vault\nData-plane isolation\nAbility to work effectively across cloud, platform, data, ML, security, and product teams.\nNice-to-Have Qualifications\nExperience deploying and operating models supporting excavation risk scoring, asset integrity, anomaly detection, or predictive maintenance.\nExperience building CI/CD workflows that promote asset-integrity or infrastructure-risk models across DEV → QA → PROD, with automated data contracts and quality gates.\nExperience implementing model and data observability for workloads using pipeline inspection, sensor, maintenance, or asset-condition data streams.\nFamiliarity with monitoring:\nData and concept drift\nModel performance\nSLOs\nPipeline health\nAlerting and incident management\nUnderstanding of data residency, security, privacy, compliance, and disaster-recovery requirements for asset-integrity, pipeline, utility, or infrastructure data used by ML services.\nSuccess Metrics\nSuccess in this role will be measured by the engineer’s ability to establish reliable, repeatable, and secure MLOps/LLMOps practices across the enterprise platform.\nKey me...","description_format":"text","description_chars":13106,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Health insurance"],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Low-Code & No-Code Platforms","Field 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