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Irth Solutions

MLOps / LLMOps Engineer (Mid-Level)

LocationIndia
Work moderemote
Typefull-time
DepartmentR&D
Company size11+ people
First seen1w ago
Last seen3d ago
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.
About the role
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:
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Damage Prevention
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Asset Integrity
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Land Management
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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.
Responsibilities
Key Responsibilities
1. Build the ML/LLM Platform on the Lakehouse
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Operationalize the complete ML lifecycle—including training, evaluation, packaging, deployment, and monitoring—on Databricks.
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Implement ML workflows using the Bronze → Silver → Gold medallion architecture with Delta Lake as the underlying storage layer.
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Establish implementation patterns for Unity Catalog model management, preparing model assets for catalog-based governance, lineage, discovery, and access control.
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Develop reusable templates for ML/LLM jobs, workflows, and deployment processes.
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Create and maintain cluster policies for ML/LLM workloads aligned with enterprise platform guardrails.
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Apply platform standards such as:
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Private networking
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Mandatory resource tagging
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Long-Term Support (LTS) Databricks Runtime versions
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Secure secrets management
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Appropriate compute policies
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Establish reusable patterns that allow Data Scientists and ML Engineers to deploy models consistently and safely.
2. Productionize ML & LLM Features
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Partner with Data Science and Product teams to productionize models supporting use cases such as:
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Excavation and infrastructure risk scoring
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Anomaly detection
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Predictive maintenance
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Geospatial enrichment
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Named Entity Recognition (NER) over parcels and easements
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Stakeholder communication summarization
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Retrieval-Augmented Generation (RAG)
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AI-powered assistants and decision-support applications
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Design, build, and maintain production-grade LLM and RAG pipelines.
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Implement vector search and retrieval architectures using technologies such as Databricks Vector Search.
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Deploy and manage model-serving and inference endpoints.
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Optimize inference workloads for performance, scalability, reliability, and cost.
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Apply optimization techniques such as:
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Quantization
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Distillation
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Prompt and response caching
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Retrieval optimization
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Batching
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Implement batch, streaming, and online inference patterns based on business and latency requirements.
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Establish clear service-level expectations and operational SLAs for Priority A/B/C workloads.
3. Engineer Reliability, Security & Compliance into the ML Lifecycle
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Integrate data contracts and quality gates into ML and LLM pipelines.
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Implement automated validation for:
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Schema drift
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Null thresholds
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Duplicate records
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Referential integrity
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Data completeness
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Feature-quality issues
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Implement PII detection, classification, masking, and obfuscation before sensitive data is consumed by features or models.
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Enforce data residency requirements through policy-as-code.
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Ensure regulated or sensitive Bronze-layer data remains in the required geographic region.
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Ensure only appropriately anonymized or aggregated data is transferred to global workspaces or services.
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Maintain end-to-end lineage across the ML lifecycle, including:Source tables/columns → Features → Models → Serving Endpoints → Applications/BI
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Surface lineage and governance information through appropriate monitoring and governance dashboards.
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Support security, compliance, audit, and access-review requirements across the ML platform.
4. Automate Everything – CI/CD & Testing
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Use Databricks Asset Bundles (DABs) and GitHub Actions to version, test, and promote ML/LLM assets across environments.
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Automate promotion across:DEV → QA → PROD
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Ensure production changes are deployed through controlled CI/CD processes with no direct development in PROD.
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Version and manage:
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Jobs
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Notebooks
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Model artifacts
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Cluster policies
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Configuration
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Permissions
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Deployment definitions
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Build automated unit, integration, regression, and data-quality test suites.
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Implement model-quality validation as part of deployment pipelines.
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Validate business KPIs and analytical outputs against the Unity Catalog semantic layer before production publication.
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Establish deployment gates that prevent models or applications from progressing when quality, security, or performance requirements are not met.
5. Observability & Production Operations
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Instrument ML/LLM pipelines and services to support defined SLOs, including:
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P1 pipeline success: ≥99.5%
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P1 MTTD: ≤5 minutes
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MTTR: ≤60 minutes
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Implement proactive monitoring, alerting, and operational dashboards.
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Integrate automated Jira ticket creation for qualifying P1 production failures.
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Monitor ML systems for:
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Model performance degradation
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Data drift
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Concept drift
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Feature-quality degradation
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Prediction distribution changes
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Establish LLM-specific observability, including:
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Hallucination rates
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Response quality
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Latency
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Token consumption
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API usage
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Inference costs
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Retrieval quality
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Define appropriate thresholds and automated alerts for model and LLM quality degradation.
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Develop and maintain production runbooks, troubleshooting procedures, and operational documentation.
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Participate in or establish appropriate on-call rotations for critical ML/LLM services.
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Develop and maintain disaster-recovery procedures aligned with tiered RTO/RPO objectives, including examples such as:
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Tier 1: RPO ≤15 minutes / RTO ≤2 hours
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Participate in DR testing and document recovery outcomes and remediation actions.
6. FinOps & Cost Management
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Enforce mandatory cost and ownership tags across ML/LLM infrastructure, including:
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Domain
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Tenant
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Environment
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Cost center
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Owner
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Ensure tags are consistently propagated across jobs, clusters, warehouses, and other applicable resources.
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Support showback and chargeback reporting for ML/LLM workloads.
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Monitor compute, storage, model-serving, and LLM/API costs.
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Identify opportunities to optimize infrastructure and inference costs without compromising service quality or SLOs.
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Establish cost visibility and budget controls for production AI workloads.
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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:
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Models and LLM applications can be deployed through repeatable, automated CI/CD processes.
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ML/LLM workloads are secure, governed, observable, and production-ready.
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Data, feature, model, and serving lineage is traceable end-to-end.
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Production services consistently meet defined reliability and performance SLOs.
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Model and LLM quality degradation is detected before it materially impacts customers.
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AI workloads are optimized for both business value and infrastructure/API cost.
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Data Scientists and Product teams can leverage standardized MLOps/LLMOps patterns rather than building bespoke deployment and monitoring solutions.
Requirements
Qualifications
Required Qualifications
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3–5 years of experience in MLOps, LLMOps, ML Engineering, Data Engineering, or platform-focused ML engineering.
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Hands-on experience with Databricks, including:
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Databricks Jobs and Workflows
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Delta Lake
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Unity Catalog
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Databricks SQL Warehouses
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Proven experience building and maintaining CI/CD pipelines for data and ML workloads using:
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GitHub Actions
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Databricks Asset Bundles (DABs)
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Environment promotion across DEV → QA → PROD
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Parameterized deployments
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Secure secrets management using Azure Key Vault (AKV), AWS KMS/Secrets Manager, or equivalent technologies
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Strong understanding of data contracts, schema governance, and automated data/feature validation.
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Experience implementing Great Expectations-style validation frameworks or equivalent rule-based data-quality solutions.
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Experience building observable production pipelines, including metrics, dashboards, alerting, and monitoring against defined SLOs such as:
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Pipeline success rate
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Data freshness
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Mean Time to Detect (MTTD)
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Mean Time to Repair (MTTR)
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Strong security-first mindset with practical experience in:
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RBAC/ABAC
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Unity Catalog security
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PII detection and obfuscation
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Private networking
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Data-access controls
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Policy-as-code for data residency
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Strong proficiency in Python and SQL.
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Working knowledge of distributed computing and job orchestration within Databricks/Spark environments.
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Ability to troubleshoot production ML/data workloads and participate in operational support and incident resolution.
Nice to have
Preferred Qualifications
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Hands-on experience with LLM/GenAI workflows, including:
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Prompt engineering
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Retrieval-Augmented Generation (RAG)
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LLM evaluation frameworks and evaluation harnesses
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AI safety and guardrails
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Retrieval and response-quality evaluation
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Latency optimization
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Token and API-cost optimization
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Experience with geospatial data and analytics, including technologies and concepts such as:
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PostGIS
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Spatial joins
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Spatial indexing and tiling
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Coordinate systems and projections
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GIS-based feature engineering
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Experience integrating Power BI with Databricks SQL Warehouses and semantic layers, including an understanding of:
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Dataset refresh SLAs
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Query concurrency
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Row-Level Security (RLS)
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Object-Level Security (OLS)
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Practical knowledge of FinOps, including:
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Resource tagging
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Budget management
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Cost monitoring
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Showback/chargeback
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Cost anomaly detection and alerting
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Knowledge of Databricks disaster-recovery patterns, including:
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Delta Lake Deep Clone
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Delta Sharing
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Cross-region recovery
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Tiered RTO/RPO strategies
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DR testing and evidence collection
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Hands-on experience with Microsoft Azure and AWS, particularly where ML and data workloads span both environments.
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Understanding of cloud-native security patterns, including:
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Private Link
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VPC/VNet connectivity and peering
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Egress restrictions
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KMS
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AWS Secrets Manager
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Azure Key Vault
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Data-plane isolation
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Ability to work effectively across cloud, platform, data, ML, security, and product teams.
Nice-to-Have Qualifications
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Experience deploying and operating models supporting excavation risk scoring, asset integrity, anomaly detection, or predictive maintenance.
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Experience building CI/CD workflows that promote asset-integrity or infrastructure-risk models across DEV → QA → PROD, with automated data contracts and quality gates.
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Experience implementing model and data observability for workloads using pipeline inspection, sensor, maintenance, or asset-condition data streams.
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Familiarity with monitoring:
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Data and concept drift
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Model performance
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SLOs
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Pipeline health
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Alerting and incident management
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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:
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Reliable promotion of ML/LLM workloads through DEV → QA → PROD using automated CI/CD.
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Consistent implementation of data contracts, validation rules, security controls, and governance requirements.
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Production pipelines meeting defined availability, freshness, MTTD, and MTTR SLOs.
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Strong observability across data, features, models, LLM applications, and serving infrastructure.
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Reduced production incidents through proactive monitoring, automated testing, and standardized deployment patterns.
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Effective management and optimization of ML/LLM infrastructure and inference costs.
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Demonstrated compliance with security, residency, lineage, and DR requirements.
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Reusable MLOps/LLMOps patterns that enable Data Science and Product teams to deploy new AI capabilities faster and more safely.
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Strong collaboration with Data Science, Data Engineering, Architecture, Security, Product, and domain teams.
Benefits
What We Offer
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Be part of a dynamic and growing company that is well-respected in its industry.
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Competitive compensation based on experience and qualifications.
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Health Insurance coverage
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