MLOps / LLMOps Engineer (Mid-Level)
Irth
IndiaremotePosted 1 month ago
Skill Required
MLOpsLLMOpsML-EngineeringData-EngineeringAI-ML-Platform-EngineeringMid-Level-ML-Ops-EngineerMLOps-Engineer-JobsMLOps-EngineerAI-ML-Ops-EngineerMachine Learningdata engineeringGitHub ActionsObservabilityEngineeringDatabricksnetworkinganalyticalanalyticssecuritybuildingTestNGdesignGitCI/CDUnityCloudGenerative AIJiraRAGandCICDAIFulltime
Key highlights
- Competitive salary based on experience and qualifications
- 3–5 years of required experience in MLOps, LLMOps, or related fields
- Comprehensive medical, dental, and vision insurance
- Hands-on experience with Databricks required
Role overview
Irth Solutions is seeking an MLOps/LLMOps Engineer to build and operationalize a multi-cloud Lakehouse platform on Databricks, enabling scalable, secure, and automated machine learning and LLM services. This role is pivotal in establishing reusable engineering patterns for data contracts, lineage, security, CI/CD, and model deployment, ensuring models and LLM applications move efficiently from experimentation to production.
Responsibilities
- Build the ML/LLM Platform on the Lakehouse, including operationalizing the complete ML lifecycle, implementing ML workflows, and establishing implementation patterns for Unity Catalog model management.
- Productionize ML & LLM Features by partnering with Data Science and Product teams to support use cases such as excavation risk scoring, anomaly detection, and predictive maintenance.
- Engineer Reliability, Security & Compliance into the ML Lifecycle by integrating data contracts, implementing automated validation, and ensuring data residency requirements.
- Automate Everything – CI/CD & Testing using Databricks Asset Bundles and GitHub Actions to version, test, and promote ML/LLM assets across environments.
- Observability & Production Operations by instrumenting ML/LLM pipelines, implementing proactive monitoring, and establishing disaster-recovery procedures.
- FinOps & Cost Management by enforcing mandatory cost and ownership tags, monitoring costs, and identifying optimization opportunities.
Requirements
- 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, and Databricks SQL Warehouses.
- Proven experience building and maintaining CI/CD pipelines for data and ML workloads using GitHub Actions, Databricks Asset Bundles, and secure secrets management.
- Strong understanding of data contracts, schema governance, and automated data/feature validation.
- Experience building observable production pipelines, including metrics, dashboards, alerting, and monitoring against defined SLOs.
- Strong security-first mindset with practical experience in RBAC/ABAC, Unity Catalog security, PII detection and obfuscation, and policy-as-code.
- 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.
Nice to have
- Hands-on experience with LLM/GenAI workflows, including prompt engineering, RAG, and LLM evaluation frameworks.
- Experience with geospatial data and analytics, including PostGIS, spatial joins, and GIS-based feature engineering.
- Experience integrating Power BI with Databricks SQL Warehouses and semantic layers.
- Practical knowledge of FinOps, including resource tagging, budget management, and cost monitoring.
- Knowledge of Databricks disaster-recovery patterns, including Delta Lake Deep Clone and cross-region recovery.
- 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 KMS.
- Experience deploying and operating models supporting excavation risk scoring, asset integrity, anomaly detection, or predictive maintenance.
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.
Additional details
- Location: Remote – India
- Department: Insights (AI/ML)
- Reports to: Data Platform & Analytics Manager
- Irth Solutions is a leading provider of cloud-based SaaS software for damage prevention, asset integrity, stakeholder engagement, and land management.
- Irth serves customers across North America and continues to expand its platform with new data-driven and AI-powered capabilities.
- 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.