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

Irth Solutions; United States; Remote; USD 19,000 - 27,000; Full time

Job summary

Irth Solutions is seeking a hands-on ML/GenAI Engineer who will contribute across the data and ML lifecycle, building a governed Databricks Lakehouse to power cross-product insights and customer-facing data products.

You will collaborate with data, platform, product, and domain teams to deploy production-ready ML and GenAI solutions, with a focus on reliability, governance, and measurable business value across Damage Prevention, Asset Integrity, Land Management, and Stakeholder Engagement.

Responsibilities

• Build and strengthen Databricks Lakehouse foundations.
• Develop and productionize ML & GenAI solutions.
• Engineer for reliability, scalability & cost.
• Advance the semantic layer & data consumption.

Job description

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.

1. 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.

1. 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.

1. 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.

1. Security, Compliance & Auditability by Default

• Implement secure data and ML architectures using RBAC and ABAC within Unity