Data Science ML/Gen AI Engineer (Mid-Level)- Orbit

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)

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.