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.

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.

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.

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.

Model performance degradation

Data drift

Concept drift

Feature-quality degradation

Prediction distribution changes

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.

3–5 years of experience in MLOps, LLMOps, ML Engineering, Data Engineering, or platform-focused ML engineering.

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)

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.

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)

Resource tagging

Budget management

Cost monitoring

Showback/chargeback

Cost anomaly detection and alerting

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.

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 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 .

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.

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 .

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.

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.

Originally posted on Himalayas