The job, in the employer's words
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.
ML/GenAI Engineer – Insights (AI/ML)
Location: Remote – India
Department: Insights (AI/ML)
Reports to: Data Platform & Analytics Manager
About the Role
Irth is building a unified and governed Databricks Lakehouse to power cross-product insights and customer-facing data products.
We are looking for a hands-on ML/GenAI Engineer who can contribute across the data and ML lifecycle—from establishing reliable, governed data foundations to rapidly prototyping and productionizing machine learning and GenAI solutions.
You will work closely with data, platform, product, and domain teams to turn data into measurable customer value across Irth’s key industries:
- Damage Prevention
- Asset Integrity
- Land Management
- Stakeholder Engagement
The ideal candidate is comfortable working across data engineering, machine learning, GenAI, MLOps, governance, and cloud platforms, with a strong focus on production reliability and business outcomes.
Key Responsibilities
1. Build and Strengthen Lakehouse Foundations
- Contribute to medallion architecture pipelines (Bronze → Silver → Gold) using Databricks.
- Implement data quality checks, validation gates, and data contracts at ingestion.
- Support column-level lineage and governance initiatives, targeting at least 95% lineage coverage.
- Help implement policy-as-code for regional data residency and sensitive-data handling.
- Ensure appropriate PII masking, obfuscation, and access controls across Silver and Gold data layers.
- Collaborate with data engineering and governance teams to improve data reliability, discoverability, and documentation.
2. Develop and Productionize ML & GenAI Solutions
- Explore, prototype, evaluate, and productionize machine learning and GenAI solutions.
- Work on use cases including:
- Forecasting
- Anomaly detection
- NLP
- Retrieval-Augmented Generation (RAG)
- LLM-powered assistants and copilots
- Predictive analytics
- Develop solutions that address measurable customer and business problems across Irth’s industry verticals.
- Package and manage models using Unity Catalog model management/registries.
- Design and implement batch and streaming inference architectures where appropriate.
- Partner with Product and business stakeholders to define success metrics, KPIs, and A/B testing strategies.
- Move successful experiments from prototype to production with clearly defined SLAs, monitoring, documentation, and operational runbooks.
3. Engineer for Reliability, Scalability & Cost
- Build production workflows, jobs, and notebooks as infrastructure/assets-as-code using Databricks Asset Bundles (DABs).
- Implement CI/CD pipelines using GitHub Actions.
- Design reliable, observable, and scalable data and ML workloads.
- Work toward defined operational SLOs, including:
- Pipeline success rate: ≥99.5%
- P1 Mean Time to Detect (MTTD): ≤5 minutes
- Mean Time to Repair (MTTR): ≤60 minutes
- Implement proactive monitoring and alerting.
- Automate incident creation and tracking through Jira where appropriate.
- Apply FinOps principles, including resource tagging, workload policies, optimization, and cost monitoring.
- Identify opportunities to improve compute performance while maintaining cost efficiency.
4. Advance the Semantic Layer & Data Consumption
- Contribute business metrics, definitions, and semantic models to Unity Catalog.
- Help establish a single source of truth for metrics consumed across BI, analytics, and applications.
- Support consumption through Power BI and Databricks AI/BI.
- Work with domain teams to develop and maintain trusted data products.
- Improve data-product quality through documentation, contracts, testing, and governance.
- Ensure analytical definitions remain consistent across products and business functions.
5. Security, Compliance & Auditability by Default
- Implement secure data and ML architectures using RBAC and ABAC within Unity Catalog.
- Follow secure networking practices, including private networking where required.
- Manage credentials and secrets using appropriate cloud key-management and secret-management services, such as Azure Key Vault (AKV) or KMS.
- Design solutions with security, privacy, and auditability built into the development lifecycle.
- Support compliance requirements across frameworks and regulations such as:
- SOC 2
- ISO 27001
- GDPR
- PIPEDA
- Produce and maintain audit evidence related to:
- Data lineage
- Access reviews
- Data retention
- Security controls
- Disaster recovery (DR) testing and drills
- Participate in governance and security reviews and remediate identified gaps.
What Success Looks Like
In this role, success means you can take a data or AI use case from idea → prototype → production → measurable business impact, while maintaining strong standards for governance, security, reliability, and cost.
You will be successful when you:
- Deliver production-ready ML and GenAI capabilities that improve customer outcomes.
- Build solutions on trusted, governed, and well-documented data.
- Maintain reliable pipelines and inference services against agreed SLOs.
- Establish strong lineage, data quality, and security practices.
- Reduce the time required to move AI experiments into production.
- Create reusable patterns for ML/GenAI development across Irth’s products and verticals.
- Partner effectively with Product, Data Engineering, Platform, and domain teams.
Requirements
Qualifications
Required Qualifications
- 3–6 years of experience in Data Science, Machine Learning, or ML Engineering, with a proven track record of taking models from development through production.
- Strong programming and data skills in Python, SQL, and Spark/PySpark.
- Hands-on experience with Databricks, including:
- Delta Lake
- Unity Catalog
- Databricks SQL (DBSQL)
- Jobs and Workflows
- Medallion architecture
- Strong understanding of ML fundamentals, including:
- Feature engineering
- Model training and selection
- Model evaluation and validation
- Model monitoring
- Data-quality monitoring
- Model and data drift detection
- Practical GenAI/LLM experience, including:
- Prompt engineering
- Retrieval-Augmented Generation (RAG)
- Vector databases/vector stores
- LLM evaluation
- AI safety and guardrails
- Understanding of LLM latency, scalability, and cost tradeoffs
- Experience implementing CI/CD for data and ML workloads, including:
- GitHub Actions
- Databricks Asset Bundles (DABs)
- DEV → QA → PROD environment promotion
- Secrets and configuration management
- Experience with data contracts and data-quality frameworks, including schema governance, automated expectations/testing, validation, and quarantine/error-handling workflows.
- Strong understanding of data security and compliance, including:
- PII handling and protection
- RBAC/ABAC
- Data residency requirements
- Policy-as-code
- Strong communication and collaboration skills, with the ability to work effectively with Product, Engineering, Data, and domain teams.
- Ability to produce clear technical documentation, including Architecture Decision Records (ADRs), runbooks, experiment reports, and operational documentation.
Preferred Qualifications
- Experience with Microsoft Azure, including:
- Azure Data Lake Storage (ADLS)
- Azure Active Directory / Microsoft Entra ID
- Azure Key Vault (AKV)
- Microsoft Fabric
- Power BI
- Experience with AWS, including:
- Amazon S3
- AWS KMS
- AWS Secrets Manager
- Amazon RDS
- DynamoDB
- Experience with geospatial data and analytics, including PostGIS, spatial joins, spatial indexing, tiling, and GIS-based feature engineering.
- Experience with streaming and real-time data, including Structured Streaming and Change Data Capture (CDC).
- Hands-on experience with MLflow and Unity Catalog Model Serving.
- Experience implementing data and ML observability, including model performance metrics, lineage dashboards, pipeline monitoring, SLA/SLO monitoring, and alerting.
- Understanding of FinOps practices, including resource tagging, budgets, cost monitoring, and cost anomaly detection.
- Familiarity with Disaster Recovery (DR), Business Continuity Planning (BCP), and resilience practices.
- Experience working in utilities, energy, infrastructure, public works, or related industries.
Nice-to-Have Qualifications
- Experience building predictive, risk-scoring, or failure-prediction models for asset integrity, including corrosion, defects, degradation, or infrastructure failure.
- Experience applying anomaly detection and time-series forecasting to pipeline inspection, sensor, maintenance, or operational data.
- Experience engineering ML features from GIS and geospatial asset data, including pipeline routes, facilities, inspection locations, and infrastructure networks.
- Experience developing risk models using pipeline, utility, or asset-integrity data.
- Understanding of regulatory, compliance, and audit-reporting requirements associated with asset integrity and infrastructure analytics.
- Experience translating analytical and ML outputs into operational risk indicators, customer-facing insights, or decision-support tools.
Benefits
What We Offer
- Be part of a dynamic and growing company that is well-respected in its industry.
- Competitive compensation based on experience and qualifications.
- Health Insurance coverage
Requirements mentioned
- Qualifications
- Required Qualifications
- 3–6 years of experience in Data Science, Machine Learning, or ML Engineering, with a proven track record of taking models from development through production.
- Strong programming and data skills in Python, SQL, and Spark/PySpark.
- Hands-on experience with Databricks, including:
- Strong understanding of ML fundamentals, including:
- Practical GenAI/LLM experience, including:
- Experience implementing CI/CD for data and ML workloads, including:
- Experience with data contracts and data-quality frameworks, including schema governance, automated expectations/testing, validation, and quarantine/error-handling workflows.
- Strong understanding of data security and compliance, including:
- Strong communication and collaboration skills, with the ability to work effectively with Product, Engineering, Data, and domain teams.
- Ability to produce clear technical documentation, including Architecture Decision Records (ADRs), runbooks, experiment reports, and operational documentation.
- Preferred Qualifications
- Experience with Microsoft Azure, including:
- Experience with AWS, including:
- Experience with geospatial data and analytics, including PostGIS, spatial joins, spatial indexing, tiling, and GIS-based feature engineering.
- Experience with streaming and real-time data, including Structured Streaming and Change Data Capture (CDC).
- Hands-on experience with MLflow and Unity Catalog Model Serving.
- Experience implementing data and ML observability, including model performance metrics, lineage dashboards, pipeline monitoring, SLA/SLO monitoring, and alerting.
- Understanding of FinOps practices, including resource tagging, budgets, cost monitoring, and cost anomaly detection.
- Familiarity with Disaster Recovery (DR), Business Continuity Planning (BCP), and resilience practices.
- Experience working in utilities, energy, infrastructure, public works, or related industries.
- Nice-to-Have Qualifications
- Experience building predictive, risk-scoring, or failure-prediction models for asset integrity, including corrosion, defects, degradation, or infrastructure failure.
- Experience applying anomaly detection and time-series forecasting to pipeline inspection, sensor, maintenance, or operational data.
- Experience engineering ML features from GIS and geospatial asset data, including pipeline routes, facilities, inspection locations, and infrastructure networks.
- Experience developing risk models using pipeline, utility, or asset-integrity data.
- Understanding of regulatory, compliance, and audit-reporting requirements associated with asset integrity and infrastructure analytics.
- Experience translating analytical and ML outputs into operational risk indicators, customer-facing insights, or decision-support tools.
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